dmdw lab record

7/28/2019 Dmdw Lab Record

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INTRODUCTION TO WEKA:

WHAT IS WEKA?

Weka is a collection of machine learning algorithms for data mining tasks. Thealgorithms can either be applied directly to a dataset or called from your own Java code. Wekacontains tools for data pre-processing, classification, regression, clustering, association rules, andvisualization. It is also well-suited for developing new machine learning schemes.Main features:

Comprehensive set of data pre-processing tools, learning algorithms and evaluationmethods

Graphical user interfaces (incl. data visualization) Environment for comparing learning algorithms

THE GUI CHOOSER:The GUI chooser is used to start different interfaces of the Weka Environment. These

interfaces can be considered different programs and they vary in form, function and purpose.Depending on the specific need whether it be simple data exploration, detailed

experimentation or tackling very large problems, these different interfaces of Weka will be more

appropriate.

This project will mainly focus on the Explorer, Experimenter and Knowledge Flow

interfaces of the Weka Environment.

The CLI is a text based interface to the Weka Environment. It is the most memory efficient interface

available in Weka.

WEKA DATA MINER

Weka is a comprehensive set of advanced data mining and analysis tools. The strength of

Weka lies in the area of classification where it covers many of the most current machine learning

(ML) approaches. The version of Weka used in this project is version 3-4-4.

At its simplest, it provides a quick and easy way to explore and analyze data. Weka is also

suitable for dealing with large data where the resources of many computers and or multi-processor

computers can be used in parallel. We will be examining different aspects of the software with a focuson its decision tree classification features.


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DATA HANDLING:

Weka currently supports 3 external file formats namely CSV, Binary and C45. Weka also

allows for data to be pulled directly from database servers as well as web servers. Its native data

format is known as the ARFF format

ATTRIBUTE RELATION FILE FORMAT (ARFF):

An ARFF (Attribute-Relation File Format) file is an ASCII text file that describes a list of

instances sharing a set of attributes. ARFF files were developed by the Machine Learning Project at

the Department of Computer Science of The University of Waikato for use with the Weka machine

learning software

OVERVIEW

ARFF files have two distinct sections. The first section is the Header information, which isfollowed the Data information.

The ARFF Header Section

The ARFF Header section of the file contains the relation declaration and attribute

declarations.

The @relation Declaration

The relation name is defined as the first line in the ARFF file. The format is:

@relation

Where is a string. The string must be quoted if the name includesSpaces.

The @attribute Declarations

Attribute declarations take the form of an ordered sequence of@attribute statements. Each

attribute in the data set has its own @attribute statement which uniquely defines the name of that

attribute and it's data type. The order the attributes are declared indicates the column position in the

data section of the file. For example, if an attribute is the third one declared then Weka expects that all

that attributes values will be found in the third comma delimited column.

The format for the @attribute statement is:

@attribute

Where the must start with an alphabetic character. If spaces are to be

included in the name then the entire name must be quoted.

The can be any of the four types currently (version 3.2.1) supported by Weka:


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numeric string date []

Where and are defined below. The keywordsnumeric, stringand date are case insensitive.

Numeric attributes

Numeric attributes can be real or integer numbers.

Nominal attributes

Nominal values are defined by providing an listing the possible values:

{, , ...}

For example, the class value of the Iris dataset can be defined as follows:

@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}

Values that contain spaces must be quoted.

String attributes

String attributes allow us to create attributes containing arbitrary textual values. This is

very useful in text-mining applications, as we can create datasets with string attributes, then write

Weka Filters to manipulate strings (like StringToWordVectorFilter). String attributes are declared as

follows:

@ATTRIBUTE LCC string

Date attributes

Date attribute declarations take the form:

@attribute date []

where is the name for the attribute and is an optional string specifying how

date values should be parsed and printed (this is the same format used by SimpleDateFormat). The

default format string accepts the ISO-8601 combined date and time format: "yyyy-MM-

dd'T'HH:mm:ss".

Dates must be specified in the data section as the corresponding string representations of the date/time

(see example below).


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ARFF Data Section

The ARFF Data section of the file contains the data declaration line and the actual instance lines.

The @data Declaration

The @data declaration is a single line denoting the start of the data segment in the file. The format is:

@data

The instance data

Each instance is represented on a single line, with carriage returns denoting the end of the instance.

Attribute values for each instance are delimited by commas. They must appear in the order that theywere declared in the header section (i.e. the data corresponding to the nth @attribute declaration isalways the nth field of the attribute).

Missing values are represented by a single question mark, as in:

@data

4.4,?,1.5,?,Iris-setosaValues of string and nominal attributes are case sensitive, and any that contain space must be

quoted, as follows:@relation LCCvsLCSH

@attribute LCC string

@attribute LCSH string

@data

AG5, 'Encyclopedias and dictionaries.;Twentieth century.'

AS262, 'Science -- Soviet Union -- History.'

AE5, 'Encyclopedias and dictionaries.'

AS281, 'Astronomy, Assyro-Babylonian.;Moon -- Phases.'

AS281, 'Astronomy, Assyro-Babylonian.; Moon -- Tables.'

Dates must be specified in the data section using the string representation specified in the attributedeclaration. For example:

@RELATION Timestamps

@ATTRIBUTE timestamp DATE "yyyy-MM-dd HH:mm:ss"

@DATA"2001-04-03 12:12:12"

"2001-05-03 12:59:55"

NOTE: All header commands start with @ and all comment lines start with %. Comment and

blank lines are ignored.

COMMA SEPARATED VALUE (CSV):

Ex:

Sno, Sname, Branch, Year Attributes

1, abc, MCA, First2, def,MCA, First Data


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Sparse Arff Structure:@relation @attribute @data{< index> < value1>, , ,.} .

Sparse Weather Arff Dataset:@relation weather@attribute outlook {sunny, overcast, rainy}@attribute temperature real@attribute humidity real@attribute windy {TRUE, FALSE}@attribute play {yes, no}@data{0 sunny,1 85,2 85,3 FALSE,4 no}

{0 sunny,1 80,2 90,3 TRUE,4 no}{0 overcast,1 83,2 86,3 FALSE,4 yes}{0 rainy,1 70,2 96,3 FALSE,4 yes}{0 rainy,1 68,2 80,3 FALSE,4 yes}{0 rainy,1 65,2 70,3 TRUE,4 no}{0 overcast,1 64,2 65,3 TRUE,4 yes}{0 sunny,1 72,2 95,3 FALSE,4 no}{0 sunny,1 69,2 70,3 FALSE,4 yes}{0 rainy,1 75,2 80,3 FALSE,4 yes}{0 sunny,1 75,2 70,3 TRUE,4 yes}{0 overcast,1 72,2 90,3 TRUE,4 yes}

Data Retrieval and Preparation

Getting the data:

There are three ways of loading data into the explorer, these are loading from a file, a

database connection and finally getting a file from web server. We will be loading the data file from a

locally stored file.

Weka supports 4 different file formats namely, CSV, C4.5, flat binary files and the native

ARFF format. To demonstrate the functionality of the explorer environment we will be loading a CSV

file and then in the following section we will preprocess the data to prepare it for analysis. To open a

local data file, click on the Open File button, and in the window that follows select the desired data

file.

Preprocess the Data:

First Method:


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Initially (in the Preprocess tab) click "open" and navigate to the directory containing the data file

(.csv or .arff). In this case we will open the above data file.


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Since the data is not in ARFF format, a dialog box will prompt you to use the convertor, as in

Figure. You can click on "Use Converter" button, and click OK in the next dialog box that appears.

Again you can click on choose button list of converters are listed below. Choose the which

converter you want and click on OK button.

.

Once the data is loaded, WEKA will recognize the attributes and during the scan of the data will

compute some basic statistics on each attribute. The left panel in above figure shows the list of

recognized attributes, while the top panels indicate the names of the base relation (or table) and the

current working relation (which are the same initially).


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Clicking on any attribute in the left panel will show the basic statistics on that attribute. For

categorical attributes, the frequency for each attribute value is shown, while for continuous attributes

we can obtain min, max, mean, standard deviation, etc.

Note that the visualization in the right bottom panel is a form of cross-tabulation across two

attributes. For example, in above Figure, the default visualization panel cross-tabulates "married" with

the "pep" attribute (by default the second attribute is the last column of the data file). You can select

another attribute using the drop down list.

Second Method:

In this method you can load data from a web server. In preprocess tab click on Open URL

button pop-up window appeared like as below.

You can give a name of web server and followed by file name then click on OK button.

Third Method:

Statisticalmeasures ofselectedattribute

AttributesVisualize


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In this method you can load from a Database. In preprocess tab click on OpenDB button then

window is appears like as.

Filtering Algorithms:

Filters transform the input dataset in some way. When a filter is selected using the Choose

button, its name appears in the line beside that button. Click that line to get a generic object editor to

specify its properties. What appears in the line is the command-line version of the filter, and the

parameters are specified with minus signs. This is a good way of learning how to use the Weka

commands directly. There are two kinds of filters unsupervised and supervised Filters.

Filters are often applied to a training dataset and then also applied to the test file. If the filter

is supervisedfor example, if it uses class values to derive good intervals for discretization

applying it to the test data will bias the results. It is the discretization intervals derived from the

trainingdata that must be applied to the test data. When using supervised filters you must be careful

to ensure that the results are evaluated fairly, an issue that does not arise with unsupervised filters.

We treat Wekas unsupervised and supervised filtering methods separately. Within each type

there is a further distinction between attribute filters, which work on the attributes in the datasets,

and instance filters, which work on the instances.

Sno Name of Function Description

1 Add Add a new attribute, whose values are all marked as

missing.

Add a new nominal attribute representing the cluster


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2 Add Cluster assigned to each instance by a given clustering algorithm.

3 Add Expression

Create a new attribute by applying a specified mathematical

function to existing attributes

4 Add Noise Change a percentage of a given nominal attributes values.

5 Cluster Membership Use a clusterer to generate cluster membership values,

which then form the new attributes.

6 Copy Copy a range of attributes in the dataset

7Discretize.

Convert numeric attributes to nominal: Specify whichattributes, number of bins, whether to optimize the numberof bins, and output binary attributes.

Use equal-width (default) or equal-frequency binning

8 First Order Apply a first-order differencing operator to a range of

numeric attributes.

9Make Indicator

Replace a nominal attribute with a Boolean attribute. Assign

value 1 to instances with a particular range of attribute

values; otherwise, assign 0. By default, the Boolean attribute

is coded as numeric.

10 MergeTwoValues Merge two values of a given attribute: Specify the index of

the two values to be merged.11 NominalToBinary Change a nominal attribute to several binary ones, one for

each value.

12 Normalize Scale all numeric values in the dataset to lie within theinterval [0,1].

13 NumericToBinary Convert all numeric attributes into binary ones: Nonzerovalues become 1.

14 Numeric Transform Transform a numeric attribute using any Java function.

15 Remove Type Remove attributes of a given type (nominal, numeric, string,or date).

16 Remove Useless Remove constant attributes, along with nominal attributesthat vary too much.

17 ReplaceMissingValues Replace all missing values for nominal and numericattributes with the modes and means of the training data.

18 Standardize Standardize all numeric attributes to have zero mean andunit variance.

19 StringToNominal Convert a string attribute to nominal.

20 Swap Values Swap two values of an attribute.

Unsupervised Instance Filters:


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Sno Name of Function Description

1 NonSparseToSparse Convert all incoming instances to sparse format

2 Normalize Treat numeric attributes as a vector and normalize it to a

given length

3 Randomize Randomize the order of instances in a dataset

4 Remove Folds Output a specified cross-validation fold for the dataset

5 Remove Misclassified Remove instances incorrectly classified according to a

specified

6 classifier useful for removing outliers

7 Remove Percentage Remove a given percentage of a dataset

8 Remove Range Remove a given range of instances from a dataset

9 RemoveWithValues Filter out instances with certain attribute values

10 Resample Produce a random sub sample of a dataset, sampling with

replacement

11 SparseToNonSparse Convert all incoming sparse instances into nonsparse

format


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SAMPLE DATASETS:

a) Weather Dataset:

Description for weather dataset(arff)

Title: weather datasetsource of informationno of attributes : 5(1 string,2 numeric,2 nominal)no. of instances: 50

Attribute Description For Waether Dataset

attribute 1:outlook(string)attribute 2:temp(numeric)attribute 3:humd(numeric)attribute 4:windy(nominal)

labels:yes,no

attribute 5:play(nominal)labels:play,noplay

Example:

Sample Waether.arff Dataset

@relation weather

@attribute outlook {sunny,overcast,rainy}

@attribute temperature numeric

@attribute humidity numeric

@attribute windy {TRUE,FALSE}

@attribute play {yes,no}

@datasunny,85,85,FALSE,no

sunny,80,90,TRUE,no

overcast,83,86,FALSE,yes

rainy,70,96,FALSE,yes


rainy,65,70,TRUE,no

overcast,64,65,TRUE,yes

Sample Wether.csv Dataset

Outlook,temp,humd,windy,playRainy,30,40,yes,playRainy,50,20,no,playSunny,60,50,yes,noplaySunny,65,70,no,noplayOvercast,40,40,yes,play.


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Bank Dataset:

Description of bank dataset

Title: bank dataset (arff)source of informationno of attributes 12(4 numarical,8 nominal)no of instances 100

Attribute Description For Bank Dataset

attribute 1:id(numeric)attribute 2:age(numeric)attribute 3:sex(nominal)

labels:male,femaleattribute 4:region(nominal)

labels:inner_city,rural,suburbn,townattribute 5:income(numeric)

attribute 6:married(nominal)labels:yes,noattribute 7:children(numeric)attribute 8:car(nominal)

labels:yes,noattribute 9:save_acct(nominal)

labels:yes,noattribute 10:current_acct(nominal)

labels:yes,noattribute 11:mortgage(nominal)

labels:yes,noattribute 12:pep(nominal)

labels:yes,no

Example:

Sample Bank.arffdataset

@relation personal Equity plan@attribute id@attribute age@attribute sex{male,female}@attribute region{inner_city,rural,suburban,town} @attribute income@attribute marrage{yes,no}

@attribute children@attribute car{yes,no}@attribute save_acct{yes,no}@attribute current_acct{yes,no}@attribute mortgage{yes,no}@attribute pep{yes,no}@data1,20,male,inner_city,10000,no,0,yes,yes,no,yes,no2,45,male,rural,50000,yes,3,yes,no,no,yes,no3,35,female,suburban,25000,yes,2,yes,no,no,yes,no4,27,male,town,30000,no,0,yes,yes,no,yes,no5,25,female,inner_city,20000,yes,2,yes,no,no,yes,no6,30,male,town,15000,no,0,yes,yes,no,yes,no


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Sample Bank.csv datasetid,age,sex,region,income,married,children,car,save_acct,current_acct,mortgage,pep1,20,male,inner_city,10000,no,0,yes,yes,no,yes,no2,45,male,rural,50000,yes,3,yes,no,no,yes,no3,35,female,suburban,25000,yes,2,yes,no,no,yes,no4,27,male,town,30000,no,0,yes,yes,no,yes,no5,25,female,inner_city,20000,yes,2,yes,no,no,yes,no6,30,male,town,15000,no,0,yes,yes,no,yes,no

German Credit Dataset:

Description of the German credit dataset.

Title: German Credit dataSource Information

Number of Instances: 1000

Number of Attributes german: 20 (7 numerical, 13 categorical)Number of Attributes german.numer: 24 (24 numerical)

Attribute Description For German

Attribute 1: (qualitative)Status of existing checking account

A11 : ... < 0 DMA12 : 0 = 200 DM /

salary assignments for at least 1 yearA14 : no checking account

Attribute 2: (numerical)Duration in month

Attribute 3: (qualitative)Credit historyA30 : no credits taken/

all credits paid back dulyA31 : all credits at this bank paid back duly

A32 : existing credits paid back duly till now

A33 : delay in paying off in the pastA34 : critical account/

other credits existing (not at this bank)

Attribute 4: (qualitative)PurposeA40 : car (new)A41 : car (used)A42 : furniture/equipmentA43 : radio/televisionA44 : domestic appliancesA45 : repairsA46 : educationA47 : (vacation - does not exist?)


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A48 : retrainingA49 : businessA410 : others

Attribute 5: (numerical)Credit amount

Attibute 6: (qualitative)Savings account/bondsA61 : ... < 100 DMA62 : 100


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Other installment plansA141 : bankA142 : storesA143 : none

Attribute 15: (qualitative)HousingA151 : rentA152 : ownA153 : for free

Attribute 16: (numerical)Number of existing credits at this bank

Attribute 17: (qualitative)JobA171 : unemployed/ unskilled - non-resident

A172 : unskilled - residentA173 : skilled employee / officialA174 : management/ self-employed/

highly qualified employee/ officer

Attribute 18: (numerical)Number of people being liable to provide maintenance for

Attribute 19: (qualitative)TelephoneA191 : noneA192 : yes, registered under the customers name

Attribute 20: (qualitative)foreign workerA201 : yesA202 : no

Relabeled values in attribute checking_statusFrom: A11 To: '


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From: A43 To: radio/tvFrom: A44 To: 'domestic appliance'From: A45 To: repairsFrom: A46 To: educationFrom: A47 To: vacationFrom: A48 To: retrainingFrom: A49 To: businessFrom: A410 To: other

Relabeled values in attribute savings_statusFrom: A61 To: '


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From: A153 To: 'for free'

Relabeled values in attribute jobFrom: A171 To: 'unemp/unskilled non res'From: A172 To: 'unskilled resident'From: A173 To: skilledFrom: A174 To: 'high qualif/self emp/mgmt'

Relabeled values in attribute own_telephoneFrom: A191 To: noneFrom: A192 To: yes

Relabeled values in attribute foreign_workerFrom: A201 To: yes

From: A202 To: no

Relabeled values in attribute classFrom: 1 To: goodFrom: 2 To: bad

@relation german_credit@attribute checking_status { '


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'0


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@attribute ' Strawberry Soda_binarized' {0,1}@attribute ' Vanilla Ice Cream_binarized' {0,1}@attribute ' Potato Chips_binarized' {0,1}@attribute ' Strawberry Yogurt_binarized' {0,1}@attribute ' Diet Soda_binarized' {0,1}@attribute ' D Cell Batteries_binarized' {0,1}@attribute ' Paper Towels_binarized' {0,1}@attribute ' Mint Chocolate Bar_binarized' {0,1}@attribute ' Salsa Dip_binarized' {0,1}@attribute ' Buttered Popcorn_binarized' {0,1}@attribute ' Cheese Crackers_binarized' {0,1}@attribute ' Chocolate Bar_binarized' {0,1}@attribute ' Rice Soup_binarized' {0,1}@attribute ' Mouthwash_binarized' {0,1}@attribute ' Sugar_binarized' {0,1}@attribute ' Cheese Flavored Chips_binarized' {0,1}@attribute ' Sweat Potatoes_binarized' {0,1}

@attribute ' Deodorant_binarized' {0,1}@attribute ' Waffles_binarized' {0,1}@attribute ' Decaf Coffee_binarized' {0,1}@attribute ' Smoked Turkey Sliced_binarized' {0,1}@attribute ' Screw Driver_binarized' {0,1}@attribute ' Sesame Oil_binarized' {0,1}@attribute ' Red Wine_binarized' {0,1}@attribute ' 60 Watt Lightbulb_binarized' {0,1}@attribute ' Cream Soda_binarized' {0,1}@attribute ' Apple Fruit Roll_binarized' {0,1}@attribute ' Noodle Soup_binarized' {0,1}@attribute ' Ice Cream Sandwich_binarized' {0,1}

@attribute ' Soda Crackers_binarized' {0,1}@attribute ' Lettuce_binarized' {0,1}@attribute ' AA Cell Batteries_binarized' {0,1}@attribute ' Honey Roasted Peanuts_binarized' {0,1}@attribute ' Frozen Cheese Pizza_binarized' {0,1}@attribute ' Tomato Soup_binarized' {0,1}@attribute ' Manicotti_binarized' {0,1}@attribute ' Toilet Bowl Cleaner_binarized' {0,1}@attribute ' Liquid Laundry Detergent_binarized' {0,1}@attribute ' Instant Rice_binarized' {0,1}@attribute ' Green Pepper_binarized' {0,1}@attribute ' Frozen Broccoli_binarized' {0,1}

@attribute ' Chardonnay Wine_binarized' {0,1}@attribute ' Brown Sugar Grits_binarized' {0,1}@attribute ' Canned Peas_binarized' {0,1}@attribute ' Skin Moisturizer_binarized' {0,1}@attribute ' Avocado Dip_binarized' {0,1}@attribute ' Blueberry Muffins_binarized' {0,1}@attribute ' Apple Cinnamon Waffles_binarized' {0,1}@attribute ' Chablis Wine_binarized' {0,1}@attribute ' Cantaloupe_binarized' {0,1}@attribute ' Shrimp Cocktail Sauce_binarized' {0,1}@attribute ' 100 Watt Lightbulb_binarized' {0,1}@attribute ' Whole Green Beans_binarized' {0,1}@attribute ' Turkey TV Dinner_binarized' {0,1}@attribute ' Wash Towels_binarized' {0,1}


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@attribute ' Dog Food_binarized' {0,1}@attribute ' Cat Food_binarized' {0,1}@attribute ' Frozen Sausage Pizza_binarized' {0,1}@attribute ' Frosted Donuts_binarized' {0,1}@attribute ' Shrimp_binarized' {0,1}@attribute ' Summer Sausage_binarized' {0,1}@attribute ' Plums_binarized' {0,1}@attribute ' Mild Cheddar Cheese_binarized' {0,1}@attribute ' Cream of Wheat_binarized' {0,1}@attribute ' Fresh Lima Beans_binarized' {0,1}@attribute ' Flavored Fruit Bars_binarized' {0,1}@attribute ' Mushrooms_binarized' {0,1}@attribute ' Flour_binarized' {0,1}@attribute ' Plain Rye Bread_binarized' {0,1}@attribute ' Jelly Filled Donuts_binarized' {0,1}@attribute ' Apple Sauce_binarized' {0,1}@attribute ' Hot Chicken Wings_binarized' {0,1}

@attribute ' Orange Juice_binarized' {0,1}@attribute ' Strawberry Jam_binarized' {0,1}@attribute ' Chocolate Chip Cookies_binarized' {0,1}@attribute ' Vegetable Soup_binarized' {0,1}@attribute ' Oats and Nuts Cereal_binarized' {0,1}@attribute ' Fruit Roll_binarized' {0,1}@attribute ' Corn Oil_binarized' {0,1}@attribute ' Corn Flake Cereal_binarized' {0,1}@attribute ' 75 Watt Lightbulb_binarized' {0,1}@attribute ' Mushroom Pizza - Frozen_binarized' {0,1}@attribute ' Sour Cream_binarized' {0,1}@attribute ' Deli Salad_binarized' {0,1}

@attribute ' Deli Turkey_binarized' {0,1}@attribute ' Glass Cleaner_binarized' {0,1}@attribute ' Brown Sugar_binarized' {0,1}@attribute ' English Muffins_binarized' {0,1}@attribute ' Apple Soda_binarized' {0,1}@attribute ' Strawberry Preserves_binarized' {0,1}@attribute ' Pepperoni Pizza - Frozen_binarized' {0,1}@attribute ' Plain Oatmeal_binarized' {0,1}@attribute ' Beef Soup_binarized' {0,1}@attribute ' Trash Bags_binarized' {0,1}@attribute ' Corn Chips_binarized' {0,1}@attribute ' Tangerines_binarized' {0,1}

@attribute ' Hot Dogs_binarized' {0,1}@attribute ' Can Opener_binarized' {0,1}@attribute ' Dried Apples_binarized' {0,1}@attribute ' Grape Juice_binarized' {0,1}@attribute ' Carrots_binarized' {0,1}@attribute ' Frozen Shrimp_binarized' {0,1}@attribute ' Grape Fruit Roll_binarized' {0,1}@attribute ' Merlot Wine_binarized' {0,1}@attribute ' Raisins_binarized' {0,1}@attribute ' Cranberry Juice_binarized' {0,1}@attribute ' Shampoo_binarized' {0,1}@attribute ' Pancake Mix_binarized' {0,1}@attribute ' Paper Plates_binarized' {0,1}@attribute ' Bologna_binarized' {0,1}


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@attribute ' 2pct. Milk_binarized' {0,1}@attribute ' Daily Newspaper_binarized' {0,1}@attribute ' Popcorn Salt_binarized' {0,1}

@data0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0


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Exp: 1 Date: _ _/_ _/_ _

Name of the Experiment: ..

..

Add:

a)SCHEMA : Weka.filters.unsupervised.attribute.AddN unnamed-C 3


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Discretize:SCHEMA : Weka.filters.unsupervised.attribute.descretize-B11-M-1.0-R first-last


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NominalToBinary:b)SCHEMA : Weka.filters.unsupervised.attribute.NominalToBinary-R first-last


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Normalize:

c)SCHEMA : Weka.filters.unsupervised.attribute.Normalize-S 1.0-T 0.0


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NumericToBinary:

d)SCHEMA : Weka.filters.unsupervised.attribute.NumericToBinary


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Swap Values:

e)SCHEMA : Weka.filters.unsupervised.attribute .Swap ValuesC lastF firsts last


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String to Nominal:

f)Weka.filters.unsupervised.attribute.StringtoNominal


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Exp: 2 Date: _ _/_ _/_ _


..

Implement Weka.Classifiers.trees.j48

Weather Dataset.arff

@relation weather

@attribute outlook {sunny, overcast, rainy}

@attribute temperature real@attribute humidity real

@attribute windy {TRUE, FALSE}

@attribute play {yes, no}

@data

sunny,85,85,FALSE,no

sunny,80,90,TRUE,no



rainy,68,80,FALSE,yesrainy,65,70,TRUE,no


sunny,72,95,FALSE,no

sunny,69,70,FALSE,yes


sunny,75,70,TRUE,yes



rainy,71,91,TRUE,no

Use Training Set Testing Options:


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=== Run information ===

Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2

Relation: weather

Instances: 14

Attributes: 5outlook

temperature

humidity

windy

play

Test mode: evaluate on training data

=== Classifier model (full training set) ===

J48 pruned tree

------------------

outlook = sunny| humidity 75: no (3.0)

outlook = overcast: yes (4.0)

outlook = rainy

| windy = TRUE: no (2.0)

| windy = FALSE: yes (3.0)

Number of Leaves : 5

Size of the tree : 8

Time taken to build model: 0.02 seconds

=== Evaluation on training set ====== Summary ===

Correctly Classified Instances 14 100 %

Incorrectly Classified Instances 0 0 %

Kappa statistic 1

Mean absolute error 0

Root mean squared error 0

Relative absolute error 0 %

Root relative squared error 0 %

Coverage of cases (0.95 level) 100 %

Mean rel. region size (0.95 level) 50 %Total Number of Instances 14

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class

1 0 1 1 1 1 yes

1 0 1 1 1 1 no

=== Confusion Matrix ===

a b


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9 0 | a = yes

0 5 | b = no

Visualize Tree:

Use Cross Validation Testing Option:

Classifier Output:




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Relation: weather

Instances: 14

Attributes: 5

outlook

temperature

humiditywindy

play

Test mode: 10-fold cross-validation


J48 pruned tree

------------------

outlook = sunny

| humidity 75: no (3.0)

outlook = overcast: yes (4.0)outlook = rainy





Time taken to build model: 0 seconds

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances 9 64.2857 %Incorrectly Classified Instances 5 35.7143 %

Kappa statistic 0.186

Mean absolute error 0.2857

Root mean squared error 0.4818

Relative absolute error 60 %

Root relative squared error 97.6586 %

Coverage of cases (0.95 level) 92.8571 %

Mean rel. region size (0.95 level) 64.2857 %

Total Number of Instances 14



0.778 0.6 0.7 0.778 0.737 0.789 yes

0.4 0.222 0.5 0.4 0.444 0.789 no


a b


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Use Supplied Test Set Testing Options:

We will now use our model to classify the new instances. A portion of the new instances ARFF

file is depicted in Figure Note that the attribute section is identical to the training data (bank data we

used for building our model). However, in the data section, the value of the "pep" attribute is "?" (or

unknown).

In the main panel, under "Test options" click the "Supplied test set" radio button, and then click the

"Set..." button. This will pop up a window which allows you to open the file containing test instances,

as in Figures


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In this case, we open the file "bank-new.arff" and upon returning to the main window,

we click the "start" button. This, once again generates the models from our training data, but this time

it applies the model to the new unclassified instances in the "bank-new.arff" file in order to predict the

value of "pep" attribute. The result is depicted in Figure 28. Note that the summary of the results in

the right panel does not show any statistics. This is because in our test instances the value of the class

attribute ("pep") was left as "?", thus WEKA has no actual values to which it can compare the

predicted values of new instances.

Of course, in this example we are interested in knowing how our model managed to

classify the new instances. To do so we need to create a file containing all the new instances along

with their predicted class value resulting from the application of the model. Doing this is much

simpler using the command line version of WEKA classifier application. However, it is possible to do

so in the GUI version using an "indirect" approach, as follows.

First, right-click the most recent result set in the left "Result list" panel. In the resulting

pop-up window select the menu item "Visualize classifier errors". This brings up a separate window

containing a two-dimensional graph.


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We would like to "save" the classification results from which the graph is generated. In

the new window, we click on the "Save" button and save the result as the file: "bank-predicted.arff".

This file contains a copy of the new instances along with an additional column for the predicted value

of "pep".


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Note that two attributes have been added to the original new instances data:

"Instance_number" and "predictedpep". These correspond to new columns in the data portion. The

"predictedpep" value for each new instance is the last value before "?" which the actual "pep" class

value. For example, the predicted value of the "pep" attribute for instance 0 is "YES" according to our

model, while the predicted class value for instance 4 is "NO".

Use Percentage Split Testing Options:

Classifiers Output:=== Run information ===


Relation: weather

Instances: 14

Attributes: 5

outlook

temperature

humidity

windy

playTest mode: split 66.0% train, remainder test


J48 pruned tree

------------------

outlook = sunny

| humidity 75: no (3.0)

outlook = overcast: yes (4.0)

outlook = rainy




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Time taken to build model: 0 seconds

=== Evaluation on test split ===

=== Summary ===

Correctly Classified Instances 2 40 %

Incorrectly Classified Instances 3 60 %

Kappa statistic -0.3636

Mean absolute error 0.6

Root mean squared error 0.7746

Relative absolute error 126.9231 %

Root relative squared error 157.6801 %

Coverage of cases (0.95 level) 40 %

Mean rel. region size (0.95 level) 50 %Total Number of Instances 5



0.667 1 0.5 0.667 0.571 0.333 yes

0 0.333 0 0 0 0.333 no


a b


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Exp: 3 Date: _ _/_ _/_ _


..

Analyzing the Output:

Run Information:

The first line of the run information section contains information about the learning scheme

chosen and its parameters. The parameters chosen (both default and modified) are shown in short

form. In our example, the learning scheme was weka.classifiers.trees.J48 or the J48 algorithm.

The second line shows information about the relation. Relations in Weka are like data

files. The name of the relation contains in it the name of data file used to build it, and the names

of filters that have been applied on it.

The next part shows the number of instances in the relation, followed by the number ofattributes. This is followed by the list of attributes.

The last part show the type of testing that was employed; in our example it was 10-fold cross-

validation

Classifier Model (full Training set):

J48 pruned tree

------------------

Outlook = sunny| Humidity 75: no (3.0)

Outlook = overcast: yes (4.0)

Outlook = rainy

| Windy = TRUE: no (2.0)

| Windy = FALSE: yes (3.0)

Number of Leaves: 5

Size of the tree: 8

It displays information about the model generated using the full training set. It mentions

full training set because we used cross-validation and what is being displayed here is the final

model that was built used all of the dataset to be generated. When using tree models, a text

display of the generated tree is shown. This is followed by the information about the number of

leaves and overall tree size (above).

Confusion Matrix:

A confusion matrix is an easy way of describing the results of the experiment. The best

way to describe it is by example


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a b


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How much does the scheme improve on simply predicting the average? The relative squared erroris

The relative absolute erroris:

A B C D

Root mean-squared error 67.8 91.7 63.3 57.4

Mean absolute error 41.3 38.5 33.4 29.2

Root rel squared error 42.2% 57.2% 39.4% 35.8%

Relative absolute error 43.1% 40.1% 34.8% 30.4%

Correlation coefficient 0.88 0.88 0.89 0.91

22

1

22

11

)(...)(

)(...)(

n

nn

aaaa

apap

||...||

||...||

1

11

n

nn

aaaa

apap


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Exp: 4 Date: _ _/_ _/_ _


..

K- Means Clustering in Weka:

This example illustrates the use ofk-means clustering with WEKA The sample data set used

for this example is based on the "bank data" available in ARFF format (bank-data.arff). As an

illustration of performing clustering in WEKA, we will use its implementation of the K-means

algorithm to cluster the customers in this bank data set, and to characterize the resulting customer

segments.Since the Data File is loaded in Weka Explorer.

Bank Data Set:

@relation bank@attribute Instance_number numeric@attribute age numeric@attribute sex {"FEMALE","MALE"}@attribute region {"INNER_CITY","TOWN","RURAL","SUBURBAN"}@attribute income numeric@attribute married {"NO","YES"}@attribute children {0,1,2,3}@attribute car {"NO","YES"}@attribute save_act {"NO","YES"}@attribute current_act {"NO","YES"}@attribute mortgage {"NO","YES"}@attribute pep {"YES","NO"}

@data0,48,FEMALE,INNER_CITY,17546,NO,1,NO,NO,NO,NO,YES1,40,MALE,TOWN,30085.1,YES,3,YES,NO,YES,YES,NO2,51,FEMALE,INNER_CITY,16575.4,YES,0,YES,YES,YES,NO,NO3,23,FEMALE,TOWN,20375.4,YES,3,NO,NO,YES,NO,NO4,57,FEMALE,RURAL,50576.3,YES,0,NO,YES,NO,NO,NO5,57,FEMALE,TOWN,37869.6,YES,2,NO,YES,YES,NO,YES6,22,MALE,RURAL,8877.07,NO,0,NO,NO,YES,NO,YES

7,58,MALE,TOWN,24946.6,YES,0,YES,YES,YES,NO,NO8,37,FEMALE,SUBURBAN,25304.3,YES,2,YES,NO,NO,NO,NO9,54,MALE,TOWN,24212.1,YES,2,YES,YES,YES,NO,NO10,66,FEMALE,TOWN,59803.9,YES,0,NO,YES,YES,NO,NO11,52,FEMALE,INNER_CITY,26658.8,NO,0,YES,YES,YES,YES,NO12,44,FEMALE,TOWN,15735.8,YES,1,NO,YES,YES,YES,YES13,66,FEMALE,TOWN,55204.7,YES,1,YES,YES,YES,YES,YES14,36,MALE,RURAL,19474.6,YES,0,NO,YES,YES,YES,NO15,38,FEMALE,INNER_CITY,22342.1,YES,0,YES,YES,YES,YES,NO16,37,FEMALE,TOWN,17729.8,YES,2,NO,NO,NO,YES,NO17,46,FEMALE,SUBURBAN,41016,YES,0,NO,YES,NO,YES,NO18,62,FEMALE,INNER_CITY,26909.2,YES,0,NO,YES,NO,NO,YES

19,31,MALE,TOWN,22522.8,YES,0,YES,YES,YES,NO,NO20,61,MALE,INNER_CITY,57880.7,YES,2,NO,YES,NO,NO,YES


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21,50,MALE,TOWN,16497.3,YES,2,NO,YES,YES,NO,NO22,54,MALE,INNER_CITY,38446.6,YES,0,NO,YES,YES,NO,NO23,27,FEMALE,TOWN,15538.8,NO,0,YES,YES,YES,YES,NO24,22,MALE,INNER_CITY,12640.3,NO,2,YES,YES,YES,NO,NO25,56,MALE,INNER_CITY,41034,YES,0,YES,YES,YES,YES,NO26,45,MALE,INNER_CITY,20809.7,YES,0,NO,YES,YES,YES,NO27,39,FEMALE,TOWN,20114,YES,1,NO,NO,YES,NO,YES28,39,FEMALE,INNER_CITY,29359.1,NO,3,YES,NO,YES,YES,NO29,61,MALE,RURAL,24270.1,YES,1,NO,NO,YES,NO,YES30,61,FEMALE,RURAL,22942.9,YES,2,NO,YES,YES,NO,NO


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In the pop-up window we enter 5 as the number of clusters (instead of the default values of 2)

and we leave the value of "seed" as is. The seed value is used in generating a random number which

is, in turn, used for making the initial assignment of instances to clusters. Note that, in general, K-

means is quite sensitive to how clusters are initially assigned. Thus, it is often necessary to try

different values and evaluate the results.

Once the options have been specified, we can run the clustering algorithm. Here we make sure

that in the "Cluster Mode" panel, the "Use Percentage Split" option is selected, and we click "Start".

We can right click the result set in the "Result list" panel and view the results of clustering in a

separate window.


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Cluster Output:

=== Run information ===Scheme: weka.clusterers.SimpleKMeans -N 5 -A "weka.core.EuclideanDistance -R first-last" -I500 -S 10

Relation: bankInstances: 600Attributes: 12

Instance_numberagesexregionincomemarriedchildrencarsave_act

current_actmortgagepep

Test mode: split 66% train, remainder test

=== Clustering model (full training set) ===

kMeans======Number of iterations: 14Within cluster sum of squared errors: 1719.2889887418955Missing values globally replaced with mean/mode

Cluster centroids:Cluster#

Attribute Full Data 0 1 2 3 4(600) (66) (112) (120) (137) (165)

===============================================================Instance_number 299.5 306.6364 265.9732 302.2333 320.292 300.1515age 42.395 40.0606 32.7589 51.475 44.3504 41.6424sex FEMALE FEMALE FEMALE FEMALE FEMALE MALEregion INNER_CITY RURAL INNER_CITY INNER_CITY TOWN INNER_CITYincome 27524.0312 26206.1992 18260.9218 34922.1563 27626.442 28873.3638married YES NO YES YES YES YESchildren 0 3 2 1 0 0car NO NO NO NO NO YESsave_act YES YES YES YES YES YEScurrent_act YES YES YES YES YES YESmortgage NO NO NO NO NO YESpep NO NO NO YES NO YES

Time taken to build model (full training data) : 0.14 seconds=== Model and evaluation on test split ===kMeans======

Number of iterations: 10Within cluster sum of squared errors: 1115.231316606429


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Missing values globally replaced with mean/mode

Cluster centroids:Cluster#

Attribute Full Data 0 1 2 3 4(396) (131) (63) (80) (41) (81)

===============================================================Instance_number 299.6364 277.1374 347.1905 362.0625 231.5122 271.8642age 43.1061 40.4733 49.1111 45.1 51.2439 36.6049sex MALE FEMALE MALE MALE FEMALE MALEregion INNER_CITY INNER_CITY INNER_CITY TOWN RURAL INNER_CITYincome 27825.983 25733.2533 32891.0238 30817.2439 32090.4995 22158.1307married YES YES YES NO NO YESchildren 0 0 1 0 0 0car NO YES YES NO YES NOsave_act YES YES YES YES YES NOcurrent_act YES YES NO YES YES YES

mortgage NO NO NO NO NO YESpep NO NO YES YES NO YES

Time taken to build model (percentage split) : 0.04 secondsClustered Instances0 73 ( 36%)1 37 ( 18%)2 30 ( 15%)3 28 ( 14%)4 36 ( 18%)

The result window shows the centroid of each cluster as well as statistics on the number andpercentage of instances assigned to different clusters. Cluster centroids are the mean vectors for each

cluster (so, each dimension value in the centroid represents the mean value for that dimension in the

cluster). Thus, centroids can be used to characterize the clusters. For example, the centroid for cluster

1 shows that this is a segment of cases representing middle aged to young (approx. 38) females living

in inner city with an average income of approx. $28,500, who are married with one child, etc.

Furthermore, this group have on average said YES to the PEP product.

Another way of understanding the characteristics of each cluster in through visualization. We

can do this by right-clicking the result set on the left "Result list" panel and selecting "Visualize

cluster assignments".


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You can choose the cluster number and any of the other attributes for each of the three

different dimensions available (x-axis, y-axis, and color). Different combinations of choices will

result in a visual rendering of different relationships within each cluster. In the above example, we

have chosen the cluster number as the x-axis, the instance number (assigned by WEKA) as the y-axis,

and the "sex" attribute as the color dimension. This will result in a visualization of the distribution of

males and females in each cluster. For instance, you can note that clusters 2 and 3 are dominated by

males, while clusters 4 and 5 are dominated by females. In this case, by changing the color dimension

to other attributes, we can see their distribution within each of the clusters.

Finally, we may be interested in saving the resulting data set which included each instance

along with its assigned cluster. To do so, we click the "Save" button in the visualization window and

save the result as the file "bank-kmeans.arff".

@relation bank_clustered

@attribute Instance_number numeric@attribute age numeric"@attribute sex {FEMALE,MALE}""@attribute region {INNER_CITY,TOWN,RURAL,SUBURBAN}"@attribute income numeric

"@attribute married {NO,YES}""@attribute children {0,1,2,3}""@attribute car {NO,YES}""@attribute save_act {NO,YES}""@attribute current_act {NO,YES}""@attribute mortgage {NO,YES}""@attribute pep {YES,NO}""@attribute Cluster {cluster0,cluster1,cluster2,cluster3,cluster4,cluster5}"

@data"0,48,FEMALE,INNER_CITY,17546,NO,1,NO,NO,NO,NO,YES,cluster1""1,40,MALE,TOWN,30085.1,YES,3,YES,NO,YES,YES,NO,cluster3""2,51,FEMALE,INNER_CITY,16575.4,YES,0,YES,YES,YES,NO,NO,cluster2""3,23,FEMALE,TOWN,20375.4,YES,3,NO,NO,YES,NO,NO,cluster5"


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"4,57,FEMALE,RURAL,50576.3,YES,0,NO,YES,NO,NO,NO,cluster5""5,57,FEMALE,TOWN,37869.6,YES,2,NO,YES,YES,NO,YES,cluster5""6,22,MALE,RURAL,8877.07,NO,0,NO,NO,YES,NO,YES,cluster0""7,58,MALE,TOWN,24946.6,YES,0,YES,YES,YES,NO,NO,cluster2""8,37,FEMALE,SUBURBAN,25304.3,YES,2,YES,NO,NO,NO,NO,cluster5""9,54,MALE,TOWN,24212.1,YES,2,YES,YES,YES,NO,NO,cluster2""10,66,FEMALE,TOWN,59803.9,YES,0,NO,YES,YES,NO,NO,cluster5""11,52,FEMALE,INNER_CITY,26658.8,NO,0,YES,YES,YES,YES,NO,cluster4""12,44,FEMALE,TOWN,15735.8,YES,1,NO,YES,YES,YES,YES,cluster1""13,66,FEMALE,TOWN,55204.7,YES,1,YES,YES,YES,YES,YES,cluster1""14,36,MALE,RURAL,19474.6,YES,0,NO,YES,YES,YES,NO,cluster5""15,38,FEMALE,INNER_CITY,22342.1,YES,0,YES,YES,YES,YES,NO,cluster2""16,37,FEMALE,TOWN,17729.8,YES,2,NO,NO,NO,YES,NO,cluster5""17,46,FEMALE,SUBURBAN,41016,YES,0,NO,YES,NO,YES,NO,cluster5""18,62,FEMALE,INNER_CITY,26909.2,YES,0,NO,YES,NO,NO,YES,cluster4""19,31,MALE,TOWN,22522.8,YES,0,YES,YES,YES,NO,NO,cluster2""20,61,MALE,INNER_CITY,57880.7,YES,2,NO,YES,NO,NO,YES,cluster2"

"21,50,MALE,TOWN,16497.3,YES,2,NO,YES,YES,NO,NO,cluster5"

Note that in addition to the "instance_number" attribute, WEKA has also added "Cluster" attribute to

the original data set. In the data portion, each instance now has its assigned cluster as the last attribute

value. By doing some simple manipulation to this data set, we can easily convert it to a more usable

form for additional analysis or processing.


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Exp: 5 Date: _ _/_ _/_ _


..

Aprior Algorithm using Weka

Market Basket Dataset:

@relation marketbasket-weka.filters.unsupervised.attribute.NumericToBinary-weka.filters.unsupervised.attribute.NumericToBinary

@attribute ' Hair Conditioner_binarized' {0,1}@attribute ' Lemons_binarized' {0,1}@attribute ' Standard coffee_binarized' {0,1}

@attribute ' Frozen Chicken Wings_binarized' {0,1}@attribute ' 98pct. Fat Free Hamburger_binarized' {0,1}@attribute ' Sugar Cookies_binarized' {0,1}@attribute ' Onions_binarized' {0,1}@attribute ' Deli Ham_binarized' {0,1}@attribute ' Dishwasher Detergent_binarized' {0,1}@attribute ' Beets_binarized' {0,1}@attribute ' 40 Watt Lightbulb_binarized' {0,1}@attribute ' Ice Cream_binarized' {0,1}@attribute ' Cottage Cheese_binarized' {0,1}@attribute ' Plain English Muffins_binarized' {0,1}@attribute ' Strawberry Soda_binarized' {0,1}

@attribute ' Vanilla Ice Cream_binarized' {0,1}@attribute ' Potato Chips_binarized' {0,1}@attribute ' Strawberry Yogurt_binarized' {0,1}@attribute ' Diet Soda_binarized' {0,1}@attribute ' D Cell Batteries_binarized' {0,1}@attribute ' Paper Towels_binarized' {0,1}@attribute ' Mint Chocolate Bar_binarized' {0,1}@attribute ' Salsa Dip_binarized' {0,1}@attribute ' Buttered Popcorn_binarized' {0,1}@attribute ' Cheese Crackers_binarized' {0,1}@attribute ' Chocolate Bar_binarized' {0,1}@attribute ' Rice Soup_binarized' {0,1}@attribute ' Mouthwash_binarized' {0,1}@attribute ' Sugar_binarized' {0,1}@attribute ' Cheese Flavored Chips_binarized' {0,1}@attribute ' Sweat Potatoes_binarized' {0,1}@attribute ' Deodorant_binarized' {0,1}@attribute ' Waffles_binarized' {0,1}@attribute ' Decaf Coffee_binarized' {0,1}@attribute ' Smoked Turkey Sliced_binarized' {0,1}@attribute ' Screw Driver_binarized' {0,1}@attribute ' Sesame Oil_binarized' {0,1}@attribute ' Red Wine_binarized' {0,1}

@attribute ' 60 Watt Lightbulb_binarized' {0,1}@attribute ' Cream Soda_binarized' {0,1}


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@attribute ' Apple Fruit Roll_binarized' {0,1}@attribute ' Noodle Soup_binarized' {0,1}@attribute ' Ice Cream Sandwich_binarized' {0,1}@attribute ' Soda Crackers_binarized' {0,1}@attribute ' Lettuce_binarized' {0,1}@attribute ' AA Cell Batteries_binarized' {0,1}@attribute ' Honey Roasted Peanuts_binarized' {0,1}@attribute ' Frozen Cheese Pizza_binarized' {0,1}@attribute ' Tomato Soup_binarized' {0,1}@attribute ' Manicotti_binarized' {0,1}@attribute ' Toilet Bowl Cleaner_binarized' {0,1}@attribute ' Liquid Laundry Detergent_binarized' {0,1}@attribute ' Instant Rice_binarized' {0,1}@attribute ' Green Pepper_binarized' {0,1}@attribute ' Frozen Broccoli_binarized' {0,1}@attribute ' Chardonnay Wine_binarized' {0,1}@attribute ' Brown Sugar Grits_binarized' {0,1}

@attribute ' Canned Peas_binarized' {0,1}@attribute ' Skin Moisturizer_binarized' {0,1}@attribute ' Avocado Dip_binarized' {0,1}@attribute ' Blueberry Muffins_binarized' {0,1}@attribute ' Apple Cinnamon Waffles_binarized' {0,1}@attribute ' Chablis Wine_binarized' {0,1}@attribute ' Cantaloupe_binarized' {0,1}@attribute ' Shrimp Cocktail Sauce_binarized' {0,1}@attribute ' 100 Watt Lightbulb_binarized' {0,1}@attribute ' Whole Green Beans_binarized' {0,1}@attribute ' Turkey TV Dinner_binarized' {0,1}@attribute ' Wash Towels_binarized' {0,1}

@attribute ' Dog Food_binarized' {0,1}@attribute ' Cat Food_binarized' {0,1}@attribute ' Frozen Sausage Pizza_binarized' {0,1}@attribute ' Frosted Donuts_binarized' {0,1}@attribute ' Shrimp_binarized' {0,1}@attribute ' Summer Sausage_binarized' {0,1}@attribute ' Plums_binarized' {0,1}@attribute ' Mild Cheddar Cheese_binarized' {0,1}@attribute ' Cream of Wheat_binarized' {0,1}@attribute ' Fresh Lima Beans_binarized' {0,1}@attribute ' Flavored Fruit Bars_binarized' {0,1}@attribute ' Mushrooms_binarized' {0,1}

@attribute ' Flour_binarized' {0,1}@attribute ' Plain Rye Bread_binarized' {0,1}@attribute ' Jelly Filled Donuts_binarized' {0,1}@attribute ' Apple Sauce_binarized' {0,1}@attribute ' Hot Chicken Wings_binarized' {0,1}@attribute ' Orange Juice_binarized' {0,1}@attribute ' Strawberry Jam_binarized' {0,1}@attribute ' Chocolate Chip Cookies_binarized' {0,1}@attribute ' Vegetable Soup_binarized' {0,1}@attribute ' Oats and Nuts Cereal_binarized' {0,1}@attribute ' Fruit Roll_binarized' {0,1}@attribute ' Corn Oil_binarized' {0,1}@attribute ' Corn Flake Cereal_binarized' {0,1}@attribute ' 75 Watt Lightbulb_binarized' {0,1}


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@attribute ' Mushroom Pizza - Frozen_binarized' {0,1}

@data0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

Clicking on the "Associate" tab will bring up the interface for the association rule

algorithms. The Apriori algorithm which we will use is the deafult algorithm selected. However, in

order to change the parameters for this run (e.g., support, confidence, etc.) we click on the text box

immediately to the right of the "Choose" button. Note that this box, at any given time, shows the

specific command line arguments that are to be used for the algorithm. The dialog box for changingthe parameters is depicted in Figure a2. Here, you can specify various parameters associated with

Apriori. Click on the "More" button to see the synopsis for the different parameters.


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WEKA allows the resulting rules to be sorted according to different metrics such as

confidence, leverage, and lift. In this example, we have selected lift as the criteria. Furthermore, we

have entered 1.5 as the minimum value for lift (or improvement) is computed as the confidence of the

rule divided by the support of the right-hand-side (RHS). In a simplified form, given a rule L => R,

lift is the ratio of the probability that L and R occur together to the multiple of the two individual

probabilities for L and R, i.e.,

lift= Pr(L,R) / Pr(L).Pr(R).

If this value is 1, then L and R are independent. The higher this value, the more likely that

the existence of L and R together in a transaction is not just a random occurrence, but because of

some relationship between them.

Here we also change the default value of rules (10) to be 100; this indicates that the program

will report no more than the top 100 rules (in this case sorted according to their lift values). The upper

bound for minimum support is set to 1.0 (100%) and the lower bound to 0.1 (10%). Apriori in WEKA

starts with the upper bound support and incrementally decreases support (by delta increments which

by default is set to 0.05 or 5%). The algorithm halts when either the specified number of rules aregenerated, or the lower bound for min. support is reached. The significance testing option is only

applicable in the case of confidence and is by default not used (-1.0).

Once the parameters have been set, the command line text box will show the new

command line. We now click on start to run the program.

Associator Output:


Scheme: weka.associations.Apriori -N 5 -T 0 -C 0.9 -D 0.05 -U 1.0 -M 0.1 -S -1.0 -c -1Relation: weather.symbolicInstances: 1000Attributes: 100

MilkTea powderEggButterJam

Pastebread

=== Associator model (full training set) ===

Apriori=======

Minimum support: 0.25 (3 instances)Minimum metric : 0.9Number of cycles performed: 15

Generated sets of large itemsets:


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Size of set of large itemsets L(1): 12



Best rules found:

1. milk=egg 4 ==> tea powder=yes 4 lift:(1.56) lev:(0.1) [1] conv:(1.43)2. milk=butter 4 ==> jam=normal 4 lift:(2) lev:(0.14) [2] conv:(2)3. bread=jam=milk 4 ==> egg=yes 4 lift:(1.56) lev:(0.1) [1] conv:(1.43)4. milk=tea powder=bread 3 ==> egg=high 3 lift:(2) lev:(0.11) [1] conv:(1.5)5. egg=+paste=milk 3 ==> bread=no 3 lift:(2.8) lev:(0.14) [1] conv:(1.93)

The panel on the left ("Result list") now shows an item indicating the algorithm that was

run and the time of the run. You can perform multiple runs in the same session each time with

different parameters. Each run will appear as an item in the Result list panel. Clicking on one of theresults in this list will bring up the details of the run, including the discovered rules in the right panel.

In addition, right-clicking on the result set allows us to save the result buffer into a separate file. In

this case, we save the output in the file bank-data-ar1.txt.

Note that the rules were discovered based on the specified threshold values for support and

lift. For each rule, the frequency counts for the LHS and RHS of each rule is given, as well as the

values for confidence, lift, leverage, and conviction. Note that leverage and lift measure similar

things, except that leverage measures the difference between the probability of co-occurrence of L and

R (see above example) as the independent probabilities of each of L and R, i.e.,

Leverage= Pr(L,R) - Pr(L).Pr(R).

In other words, leverage measures the proportion of additional cases covered by both L

and R above those expected if L and R were independent of each other. Thus, for leverage, values

above 0 are desirable, whereas for lift, we want to see values greater than 1. Finally, conviction is

similar to lift, but it measures the effect of the right-hand-side not being true. It also inverts the ratio.

So, convictions is measured as:

Conviction = Pr (L).Pr(not R) / Pr(L,R).

Thus, conviction, in contrast to lift is not symmetric (and also has no upper bound).

In most cases, it is sufficient to focus on a combination of support, confidence, and

either lift or leverage to quantitatively measure the "quality" of the rule. However, the real value of a

rule, in terms of usefulness and action ability is subjective and depends heavily of the particular

domain and business objectives.


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Exp: 6 Date: _ _/_ _/_ _


..

Using The Experimenter

The Experimenter interface to Weka is specialized for conducting experiments where the

user is interested in comparing several learning schemes on one or several datasets. As its output, the

Experimenter produces data that can be used to compare these learning schemes visually and

numerically as well as conducting significance testing. To demonstrate how to use the Experimenter,

we will conduct an experiment comparing two tree-learning algorithms on the birth dataset.

There are 3 main areas in the Experimenter interface and they are accessed via tabs at the top

left of the window. These 3 areas are the Setup area where the experiment parameters are set, the Run

area where the experiment is started and its progress monitored and lastly the Analyze area where the

results of the experiment are studied.

Setting up the Experiment

The Setup window has 6 main areas that must each be configured in order for the experiment

to be properly configured. Starting from the top these areas are Experiment Configuration Mode,

Results Destination, Experiment Type, Iteration Control, Datasets, Algorithms and lastly the Notes

area.

Click on New Button to CreateNew Experiment

Click on Add New Button to add aDataset used to comparingalgorithms

Click on Add New Button to addClassification algorithms forcomparing

Choose any Testing Option

Click on Browse Button toBrowse the Result arff (or) csv


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Experiment Configuration Mode:

We will be using the simple experimental interface mode, as we do not require the extra

features the advanced mode offers. We will start by creating a new experiment and then defining its

parameters. A new experiment is created by pushing the on the New button at the top of the window

and this will create a blank new experiment. After we have finished setting up the experiment, we

save it using the Save button. Experiment settings are saved in either EXP or a more familiar XML

format. These files can be opened later to recall all the experiment configuration settings.

Choose Destination:

The results of the experiment will be stored in a datafile. This area allows one to specify the

name and format that this file will have. It is not necessary to do this if one does not intend on using

this data outside of the Experimenter and if this data does not need to be examined at a later date.

Results can be stored in the ARFF or CSV format and they can also be sent to an external database.

Set Experiment Type:

There are 3 types of experiments available in the simple interface. These types vary in how

the data is going to be split for the training/testing in the experiment. The options are cross-validation,

random split and random split with order preserved (i.e. data is split randomly but the order of the

instances is not randomized; so it will instance#1 followed by instance #2 and so on). We will use

cross-validation in our example

Iteration Control:

For the randomized experiment types, the user has the option of randomizing the data again

and repeating the experiment. This value for Number of Repetitions controls how many times thiswill take place.

Add data set(s):

In this section, the user adds the datasets that will be used in the experiment. Only ARFF files

can be used here and as mentioned before, the Experimenter is expecting a fully prepared and cleaned

dataset. There is no option for choosing the classification variable here, and it will always pick the last

attribute to be the class attribute. In our example, the birth-weight data set is the only one we will use.

Add Algorithms:

In this section, the user adds the classification algorithms to be employed in the experiment.

The procedure here to select an algorithm and choose its options is exactly the same as in the

Explorer. The difference here is that more than one algorithm can be specified.Algorithms are added by clicking on the add button in the Algorithm section of the window

and this will pop-up a window that user will use to select the algorithm. This window will also display

the available options for the selected algorithm.

The first time the window displays, the ZeroR rule algorithm will be selected. This is shown

on the picture above. The user can select a different algorithm by clicking on the Choose button.

Clicking on the More button will display help about the selected Algorithm and a description of its

available options. For our example, we will add the J48 algorithm with the option for binary splits

turned on and the REPTree algorithm. Individual algorithms can be edited or deleted by clicking on

the algorithm from the list of algorithms and then by clicking on the Edit or Delete buttons. Finally,

any extra notes or comments about the experiment setup can be added by clicking on the Notes button

at the bottom of the window and entering the information in the window provided.Saving the

Experiment Setup:


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At this point, we have entered all the necessary options to start our experiment. We will now

save the experiment setup so that we do not have to re-enter all this information again. This is done by

clicking on the Save Options button on the bottom of the window. These settings can be loaded at

another time if one wishes to redo the experiment or modify it.

Running the Experiment

Running The next step is to run the experiment and this is done by clicking on the Run tab at the top

of the window. There is not much involved in this step, all that is needed is to click on the Start

button. The progress of the experiment is displayed the Status area at the bottom of the window and

any errors reported will be displayed in the Log area. Once the experiment has been run, the next step

is to analyze the results.


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Analyzing the output:

Click on Experiment Button toanalyze the Experiment

Choose Comparison Field used to

compare algorithm


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Sort Dataset in ascending order

Choose Test Base any one ingiven list


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Test Output:

Tester: weka.experiment.PairedCorrectedTTesterAnalysing: Percent_correctDatasets: 1

Resultsets: 3Confidence: 0.05 (two tailed)Sorted by: -Date: 4/6/12 9:55 PM

Dataset (1) trees.J4 | (2) trees (3) trees------------------------------------------------------------weather.symbolic (525) 50.03 | 60.03 69.02------------------------------------------------------------

(v/ /*) | (0/1/0) (0/1/0)

Key:

(1) trees.J48 '-C 0.25 -M 2' -217733168393644444(2) trees.REPTree '-M 2 -V 0.0010 -N 3 -S 1 -L -1 -I 0.0' -9216785998198681299(3) trees.RandomTree '-K 0 -M 1.0 -S 1' 8934314652175299374

dmdw lab record

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