Download - 2a. Basic Data Mining Techniques
-
8/2/2019 2a. Basic Data Mining Techniques
1/39
Basic Data Mining Techniques
-
8/2/2019 2a. Basic Data Mining Techniques
2/39
Prediction Methods
Use some variables to predict unknown or future values of
other variables.
Description Methods
Find human-interpretable patterns that describe the data.
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
-
8/2/2019 2a. Basic Data Mining Techniques
3/39
Classification [Predictive]
Deviation Detection [Predictive]
Association Rule Discovery [Descriptive]
-
8/2/2019 2a. Basic Data Mining Techniques
4/39
Given a collection of records (training set ),
the class. Find a model for class attribute as a function of the
va ues o ot er attr utes.
Goal: previously unseen records should be assigned aclass as accuratel as ossible. A test set is used to determine the accuracy of the model.
Usually, the given data set is divided into training and test sets,with training set used to build the model and test set used to
validate it.
-
8/2/2019 2a. Basic Data Mining Techniques
5/39
Tid Refund MaritalStatus
TaxableIncome Cheat
1 Yes Single 125K No
Refund Marital
Status
Taxable
Income Cheat
No Single 75K ?
2 No Married 100K No
3 No Single 70K No
4 Yes Married 120K No
Yes Married 50K ?
No Married 150K ?
Yes Divorced 90K ?
o vorce es
6 No Married 60K No
7 Yes Divorced 220K No
o ng e 40
No Married 80K ?10
TestSet
9 No Married 75K No
10 No Single 90K Yes10
TrainingSet
ModelLearn
Classifier
-
8/2/2019 2a. Basic Data Mining Techniques
6/39
Direct Marketing
to buy a new cell-phone product. Approach:
Use the data for a similar product introduced before.
We know which customers decided to buy and which decided
otherwise. This {buy, dont buy} decision forms the class attribute. o ect various emograp ic, i esty e, an company-interaction re ate
information about all such customers.
Type of business, where they stay, how much they earn, etc.
se s n orma on as npu a r u es o earn a c ass er mo e .
From [Berry & Linoff] Data Mining Techniques, 1997
-
8/2/2019 2a. Basic Data Mining Techniques
7/39
Fraud Detection
.
Approach: Use credit card transactions and the information on its account-holder
as attributes.
When does a customer buy, what does he buy, how often he payson time, etc
Label past transactions as fraud or fair transactions. This forms the classa r u e.
Learn a model for the class of the transactions.
Use this model to detect fraud by observing credit card transactions onan account.
-
8/2/2019 2a. Basic Data Mining Techniques
8/39
Customer Attrition/Churn:
competitor. Approach:
Use detailed record of transactions with each of the past and
present customers, to find attributes.
How often the customer calls, where he calls, what time-of-the day he calls most, his financial status, marital status, etc.
Label the customers as loyal or disloyal.
Find a model for loyalty.
From [Berry & Linoff] Data Mining Techniques, 1997
-
8/2/2019 2a. Basic Data Mining Techniques
9/39
Sky Survey Cataloging
Goal: To redict class star or alax of sk ob ects, es eciall
visually faint ones, based on the telescopic survey images (fromPalomar Observatory).
3000 images with 23,040 x 23,040 pixels per image.
Approach:
Segment the image.- .
Model the class based on these features.
Success Story: Could find 16 new high red-shift quasars, some of the
farthest ob ects that are difficult to find!
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
-
8/2/2019 2a. Basic Data Mining Techniques
10/39
Given a set of data points, each having a set of attributes,
and a similarity measure among them, find clusters such
that Data points in one cluster are more similar to one another.
another.
Similarity Measures: Euclidean Distance if attributes are continuous.
Other Problem-specific Measures.
-
8/2/2019 2a. Basic Data Mining Techniques
11/39
Euclidean Distance Based Clustering in 3-D space.
Intracluster distancesare minimized
Intracluster distancesare minimized
Intercluster distancesare maximized
Intercluster distancesare maximized
-
8/2/2019 2a. Basic Data Mining Techniques
12/39
Market Segmentation:
Goal: subdivide a market into distinct subsets of customers whereany subset may conceivably be selected as a market target to bereached with a distinct marketing mix.
Approach:
Collect different attributes of customers based on their geographical andlifestyle related information.
Find clusters of similar customers. easure t e c uster ng qua ty y o serv ng uy ng patterns o customers
in same cluster vs. those from different clusters.
-
8/2/2019 2a. Basic Data Mining Techniques
13/39
Document Clustering:
other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each
document. Form a similarity measure based on the
frequencies of different terms. Use it to cluster.
Gain: Information Retrieval can utilize the clusters to relate a
new document or search term to clustered documents.
-
8/2/2019 2a. Basic Data Mining Techniques
14/39
Clustering Points: 3204 Articles of Los Angeles Times.
Similarit Measure: How man words are common in these
documents (after some word filtering).
Ar t i cl es Pl aced
Financial 555 364
National 273 36
Sports 738 573
Entertainment 354 278
-
8/2/2019 2a. Basic Data Mining Techniques
15/39
Observe Stock Movements every day. Clustering points: Stock-{UP/DOWN}
described by them frequently happen together on the same day.We used association rules to quantify a similarity measure.
1Applied-Matl-DOWN,Bay-Network-Down,3-COM-DOWN,
Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN,
DSC-Comm-DOW N,INTEL-DOWN,LSI-Logic-DOWN,
Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down,Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOWN,
Technology1-DOWN
Sun-DOW N
2Apple-Comp-DOWN,Autodesk-DOWN,DEC-DOWN,
ADV-Micro-Device-DOWN,Andrew-Corp-DOWN,
Computer-Assoc-DOWN,Circuit-City-DOWN,
Compaq-DOWN, EMC-Corp-DOWN, Gen-Ins t-DOWN,
Motorola-DOW N,Microsoft-DOWN,Scientific-Atl-DOWN
Technology2-DOWN
3Fannie-Mae-DOWN,Fed-Home-Loan-DOWN,
MBNA-Corp-DOWN,Morgan-Stanley-DOWN Financial-DOWN
4Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP,
Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP,
Schlumberger-UPOil-UP
-
8/2/2019 2a. Basic Data Mining Techniques
16/39
Given a set of records each of which contain some number of items
from a given collection;
Produce dependency rules which will predict occurrence of an item
based on occurrences of other items.
TID Items
1 Bread, Coke, Milk
2 Beer, Bread
3 Beer, Coke, Diaper, Milk
4 Beer, Bread, Diaper, Milk
{ Milk } - -> { Coke}
{ Diaper, Mi lk} - -> { Beer}
{ Milk } - -> { Coke}
{ Diaper, Mi lk} - -> { Beer}
5 Coke, Diaper, Milk
-
8/2/2019 2a. Basic Data Mining Techniques
17/39
Marketing and Sales Promotion:
{Bagels, } --> {Potato Chips} Potato Chips as consequent => Can be used to determine what
.
Bagels in the antecedent => Can be used to see which products
would be affected if the store discontinues selling bagels.used to see what products should be sold with Bagels to promotesale of Potato chips!
-
8/2/2019 2a. Basic Data Mining Techniques
18/39
Supermarket shelf management.
many customers. Approach: Process the point-of-sale data collected with
barcode scanners to find dependencies among items.
A classic rule --
If a customer bu s dia er and milk then he is ver likel to bu
beer.
So, dont be surprised if you find six-packs stacked next to diapers!
-
8/2/2019 2a. Basic Data Mining Techniques
19/39
Association Rule Discovery: Application 3
Inventory Management:
Goal: A consumer a liance re air com an wants to antici ate the
nature of repairs on its consumer products and keep the service
vehicles equipped with right parts to reduce on number of visits to
consumer households.
Approach: Process the data on tools and parts required in previous
repairs at different consumer locations and discover the co-
occurrence atterns.
-
8/2/2019 2a. Basic Data Mining Techniques
20/39
Sequential Pattern Discovery: Definition
Given is a set ofobjects, with each object associated with its own timeline of events, findrules that predict strong sequential dependencies among different events.
(A B) (C) (D E)
Rules are formed by first disovering patterns. Event occurrences in the patterns aregoverned by timing constraints.
(A B) (C) (D E)
-
8/2/2019 2a. Basic Data Mining Techniques
21/39
Sequential Pattern Discovery: Examples
In telecommunications alarm logs,
(Inverter_Problem Excessive_Line_Current)
(Rectifier_Alarm) --> (Fire_Alarm)
In point-of-sale transaction sequences,
Computer Bookstore:
(Intro_To_Visual_C) (C++_Primer) -->
(Perl_for_dummies,Tcl_Tk)
Athletic Apparel Store:(Shoes) (Racket, Racketball) --> (Sports_Jacket)
-
8/2/2019 2a. Basic Data Mining Techniques
22/39
Predict a value of a given continuous valued variable based on the
values of other variables, assuming a linear or nonlinear model of
dependency.
Greatly studied in statistics, neural network fields.
Predicting sales amounts of new product based on advetising
expenditure. Predicting wind velocities as a function of temperature, humidity,
air pressure, etc.
Time series prediction of stock market indices.
-
8/2/2019 2a. Basic Data Mining Techniques
23/39
Deviation/Anomaly Detection
Detect significant deviations from normal behavior
Applications:
Credit Card Fraud Detection
Network Intrusion
Detection
Typical network traffic at University level may reach over 100 million connections per day
-
8/2/2019 2a. Basic Data Mining Techniques
24/39
Classical View
Exemplar View
To help categorize the data mining technique
Roiger, 2003
-
8/2/2019 2a. Basic Data Mining Techniques
25/39
All concepts have definite defining properties.
Example:
annual income >= 30,000
& years at current position >= 5 owns ome = true
Then
Good Credit Risk = true
-
8/2/2019 2a. Basic Data Mining Techniques
26/39
Concepts are represented by properties that are
probable of concept number
Example
A good credit risk might look like:
The mean annual income for individuals who consistently make loan
payments on time is $30,000Most individuals who are good credit risks have been working forthe same company for at least 5 years
The majority of good credit risks own their own home
omeowner wit an annua incomeo , emp oye at t esame position for 4 years might be classified as a good credit riskwith a probability of 0.85
-
8/2/2019 2a. Basic Data Mining Techniques
27/39
People store and recall likely concept exemplars that are
Exemplar#1
Annual income = 32,000
Number of years at current position = 6
Exemplar#2
Annual income = 52,000
Number of years at current position = 16
-
8/2/2019 2a. Basic Data Mining Techniques
28/39
Supervised Learning
.
Use the model to determine the outcome new instances ofunknown origin
Unsupervised Learning
ata m n ng met o t at u s mo e s rom ata w t out
predefined classes.
-
8/2/2019 2a. Basic Data Mining Techniques
29/39
Supervised Learning:
-
8/2/2019 2a. Basic Data Mining Techniques
30/39
Decision Tree
A tree structure where non-terminal nodes
terminal nodes reflect decision outcomes.
-
8/2/2019 2a. Basic Data Mining Techniques
31/39
Table 1.1 H othetical Trainin Data for Disease Dia nosis
Patient Sore SwollenID# Throat Fever Glands Congestion Headache Diagnosis
1 Yes Yes Yes Yes Yes Strep throat2 No No No Yes Yes Allergy3 Yes Yes No Yes No Cold
4 Yes No Yes No No Strep throat5 No Yes No Yes No Cold6 No No No Yes No Allergy7 No No Yes No No Strep throat8 Yes No No Yes Yes Allergy9 No Yes No Yes Yes Cold
Input Attributes Output Attributes
-
8/2/2019 2a. Basic Data Mining Techniques
32/39
Swollen
Glands
No Yes
Fever
Diagnosis = Strep Throat
Yes
Diagnosis = Allergy Diagnosis = Cold
No
-
8/2/2019 2a. Basic Data Mining Techniques
33/39
Table 1.2 Data Instances with an Unknown Classification
Patient Sore SwollenID# Throat Fever Glands Congestion Headache Diagnosis
11 No No Yes Yes Yes ?12 Yes Yes No No Yes ?13 No No No No Yes ?
-
8/2/2019 2a. Basic Data Mining Techniques
34/39
Translation from decision tree
Form:
IF antecedant conditions
THEN consequent conditions
Example
IF Swollen Glands = YesTHEN Diagnosis = Strep Throat
IF Swollen Glands = No & Fever = Yes
THEN Diagnosis = Cold
IF Swollen Glands = No & Fever = NoTHEN Diagnosis = Allergy
-
8/2/2019 2a. Basic Data Mining Techniques
35/39
The Acme Investors Dataset & Supervised
1. Can I develop a general profile of an online investor?
2. Can I determine if a new customer is likely to open a margin account?
3. an u a mo e pre c e average num er o ra es per mon or
a new investor?4. What characteristics differentiate female and male investors?
Output attribute:No 1. transaction method
No 2. margin accountNo 3. trades/month
No 4. sex
-
8/2/2019 2a. Basic Data Mining Techniques
36/39
The Acme Investors Dataset & Unsupervised
1. What attribute similarities group customers of Acme
2. What differences in attribute values segment thecustomer database?
-
8/2/2019 2a. Basic Data Mining Techniques
37/39
Require us to provide an initial best estimate about the
OR Use an al orithm to determine a best number of clusters
In either case, a clusterin s stem will attem t to rouinstances into clusters of significant interests
-
8/2/2019 2a. Basic Data Mining Techniques
38/39
-
8/2/2019 2a. Basic Data Mining Techniques
39/39
Dunham chapter 1.2