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Geethanjali College of Engineering and Technology

DEPARTMENT OF INFORMATION TECHNOLOGY

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*If it is prepared first time 1 , if it is updated 2 or 3

**GCET/Dept./3 indicates 3rdyear; ! indicates fourt" in t"e #ist of $%T& 'y##a(us (oo)

SYLLABUS

UNIT-IINDTODUCTION:Fundamentals of data mining, Data Mining Functionalities,

Classification of Data Mining systems, Major issues in Data Mining.

Data Preprocessing : Needs Preprocessing the Data, Data Cleaning, Data Integration and

Transformation, Data eduction, Discreti!ation and Concept "ierarchy #eneration.

UNIT-II

Data $arehouse and %&'P Technology for Data Mining Data $arehouse, Multidimensional

Data Model, Data $arehouse 'rchitecture, Data $arehouse

Implementation, Further De(elopment of Data Cu)e Technology, From Data $arehousing to

Data Mining.

UNIT-III

DATA MINING PRIMITIVES, LANGUAGES AND SYSTEM ARCHITECTURES:Data

Mining Primiti(es, Data Mining *uery &anguages, Designing #raphical +ser Interfaces ased

on a Data Mining *uery &anguage 'rchitectures of Data Mining -ystems.

UNIT-IV

CONCEPTS DESCRIPTION : Characteri!ation and Comparison : Data #enerali!ation and

-ummari!ation ased Characteri!ation, 'nalytical Characteri!ation: 'nalysis of 'ttri)ute

ele(ance, Mining Class Comparisons: Discriminating )et/een Different Classes, Mining

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Descripti(e -tatistical Measures in &arge Data)ases.

UNIT-V

MINING ASSSOCIATION RULES IN LARGE DATABASES:'ssociation ule Mining,Mining -ingleDimensional oolean 'ssociation ules from Transactional Data)ases, Mining

Multile(el 'ssociation ules from Transaction Data)ases, Mining Multidimensional 'ssociation

ules from elational Data)ases and Data $arehouses, From 'ssociation Mining to Correlation

'nalysis, Constraintased 'ssociation Mining.

UNIT-VI

CLASSIFICATION AND PREDICTION:Issues egarding Classification and Prediction,

Classification )y Decision Tree Induction, ayesian Classification, Classification )yac0propagation, Classification ased on Concepts from 'ssociation ule Mining, %ther

Classification Methods, Prediction, Classifier 'ccuracy.

UNIT-VII

CLUSTER ANALYSIS INTRODUCTION:Types of Data in Cluster 'nalysis, '

Categori!ation of Major Clustering Methods, Partitioning Methods, Densityased Methods,

#ridased Methods, Modelased Clustering Methods, %utlier 'nalysis.

UNIT-VIII

MINING COMPLEX TYPES OF DATA: Multimensional 'nalysis and Descripti(e Mining of

Comple1, Data %)jects, Mining -patial Data)ases, Mining Multimedia Data)ases, Mining Time

-eries and -e2uence Data, Mining Te1t Data)ases, Mining the $orld $ide $e).

TEXT BOOKS :

3. Data Mining 4 Concepts and Techni2ues 5I'$6I "'N 7 MIC"6&IN6

8'M6 "arcourt India.

REFERENCES :

3. Data Mining Introductory and ad(anced topics 4M'#'6T " D+N"'M,

P6'-%N 6D+C'TI%N

9. Data Mining Techni2ues 4 '+N 8 P+5'I, +ni(ersity Press.

. Data $arehousing in the eal $orld 4 -'M 'N'"%; 7 D6NNI-

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M+';. Pearson 6dn 'sia.

< Data $arehousing Fundamentals 4 P'+&'5 P%NN'I'" $I&6; -T+D6NT

6DITI%N.

=. The Data $arehouse &ife cycle Tool 0it 4 '&P" 8IM'&& $I&6;

-T+D6NT 6DITI%N.

For more de!"#$, %"$" H&:''((()*+)

GEETHANALI COLLEGE OF ENGINEERING . TECHNOLOGY

CHEERYAL /V0 KEESARA /M0 RR D"$r"1)

De&!rme+ o2: IT

Ye!r !+d Seme$er o 34om S5*e1 "$ O22ered: III BTe14, IISem

N!me o2 4e S5*e1: D!!(!re4o$"+6 A+d D!! M"+"+6

N!me o2 4e F!1#7:Y)RAU De$"6+!"o+: A$$o) Pro2e$$or

De&!rme+: IT

8)8) I+rod1"o+ o 4e $5*e1:

Data mining, the extraction of hidden predictive information from large databases, is

a po/erful ne/ technology /ith great potential to help companies focus on the most

important information in their data /arehouses. Data mining tools predict future trends and

)eha(iors, allo/ing )usinesses to ma0e proacti(e, 0no/ledgedri(en decisions. The

automated, prospecti(e analyses offered )y data mining mo(e )eyond the analyses of past

e(entsprovided by retrospective tools typical of decision support systems. Data mining tools

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canans/er )usiness 2uestions that traditionally /ere too time consuming to resol(e. They

scour data)ases for hidden patterns, finding predicti(e information that e1perts may miss

)ecause it lies outside their e1pectations.

Most companies already collect and refine massi(e 2uantities of data. Data mining

techni2ues can )e implemented rapidly on e1isting soft/are and hard/are platforms to enhance

the (alue of e1isting information resources, and can )e integrated /ith ne/ products and systems

as they are )rought online. $hen implemented on high performance client>ser(er or parallel

processing computers, data mining tools can analy!e massi(e data)ases to deli(er ans/ers to

2uestions such as, ?$hich clients are most li0ely to respond to my ne1t promotional mailing, and

/hy@?

This /hite paper pro(ides an introduction to the )asic technologies of data mining.

61amples of profita)le applications illustrate its rele(ance to todayAs )usiness en(ironment as

/ell as a )asic description of ho/ data /arehouse architectures can e(ol(e to deli(er the (alue of

data mining to end users.

8)9)O5*e1"%e$ o2 4e $5*e1

Im&ro%e !#"7 o2 D!!

-ince a common D-- deficiency is ?dirty data,? it is almost guaranteed that you /ill ha(e

to address the 2uality of your data during e(ery data /arehouse iteration. Data cleansing is a

stic0y pro)lem in data /arehousing. %n one hand, a data /arehouse is supposed to pro(ide

clean, integrated, consistent and reconciled data from multiple sources. %n the other hand, /e are

faced /ith a de(elopment schedule of B39 months. It is almost impossi)le to achie(e )oth/ithout ma0ing some compromises. The difficulty lies in determining /hat compromises to

ma0e. "ere are some guidelines for determining your specific goal to cleanse your source data:

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Ne%er r7 o 1#e!+$e ALL 4e d!!)6(eryone /ould li0e to ha(e all the data perfectly

clean, )ut no)ody is /illing to pay for the cleansing or to /ait for it to get done. To clean it all

/ould simply ta0e too long. The time and cost in(ol(ed often e1ceeds the )enefit.

Ne%er 1#e!+$e NOTHING)In other /ords, al/ays plan to clean something. 'fter all,

one of the reasons for )uilding the data /arehouse is to pro(ide cleaner and more relia)le data

than you ha(e in your e1isting %&TP or D-- systems.

Deerm"+e 4e 5e+e2"$ o2 4!%"+6 1#e!+ d!!) 61amine the reasons for )uilding the data

/arehouse:

Do you ha(e inconsistent reports@

$hat is the cause for these inconsistencies@

Is the cause dirty data or is it programming errors@

$hat dollars are lost due to dirty data@

$hich data is dirty@

Deerm"+e 4e 1o$ 2or 1#e!+$"+6 4e d!!) efore you ma0e cleansing all the dirty data

your goal, you must determine the cleansing cost for each dirty data element. 61amine ho/ long

it /ould ta0e to perform the follo/ing tas0s:

'naly!e the data

Determine the correct data (alues and correction algorithms

$rite the data cleansing programs

Correct the old files and data)ases if appropriate

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Com&!re 1o$ 2or 1#e!+$"+6 o do##!r$ #o$ 57 #e!%"+6 " d"r7) 6(erything in )usiness

must )e costjustified. This applies to data cleansing as /ell. For each data element, compare the

cost for cleansing it to the )usiness loss )eing incurred )y lea(ing it dirty and decide /hether to

include it in your data cleansing goal. If dollars lost e1ceeds the cost of cleansing, put the data on

the ?to )e cleansed? list. If cost for cleansing e1ceeds dollars lost, do not put the data on the ?to

)e cleansed? list.

Pr"or"";e 4e d"r7 d!! 7o 1o+$"dered 2or 7or d!! 1#e!+$"+6 6o!#) ' difficult part

of compromising is )alancing the time you ha(e for the project /ith the goals you are trying to

achie(e. 6(en though you may ha(e )een cautious in selecting dirty data for your cleansing goal,

you may still ha(e too much dirty data on your ?to )e cleansed? list. Prioriti!e your list.

For e!14 &r"or"";ed d"r7 d!! "em !$

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M"+"m";e I+1o+$"$e+ Re&or$

'ddressing another common complaint a)out current D-- en(ironments, namely

inconsistent reports, /ill most li0ely )ecome one of your data /arehouse goals. Inconsistent

reports are mainly caused )y misuse of data, and the primary reason for misuse of data is

disagreement or misunderstanding of the meaning or the content of data. Correcting this pro)lem

is another predicament in data /arehousing, )ecause it re2uires the interested )usiness units to

resol(e their disagreements or misunderstandings. This type of effort has more than once

torpedoed a data /arehouse project )ecause it too0 too long to resol(e the disputes. Ignoring the

issue is not a solution either. $e suggest the follo/ing guidelines:

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8)>) NTU S7##!5$ ("4 Add""o+!# To&"1$

-

.no

+NIT

N%

Topic 'dditional

Topics

3 3 Introduction : Fundamentals of data

mining,

Data Mining Functionalities

Classification of Data Mining systems,

Major issues in DataMining.

Data Preprocessing : Needs Preprocessing

the Data

Data Cleaning, Data Integration and

Transformation

Dataeduction

Discreti!ationandConcept

"ierarchy#eneration

9 9 Data $arehouse and %&'P Technology for

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Data Mining Data $arehouse,

Multidimensional Data Model

Data $arehouse 'rchitecture,

Data$arehouseImplementation,

Further De(elopment of Data Cu)e

Technology

From Data $arehousing to Data Mining.

+NITIII

Data Mining Primiti(es

&anguages Testing methods

-ystem'rchitectures lac0 )o1 testing

Data Mining Primiti(es

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Data Mining *uery &anguages

Designing #raphical +ser Interfaces

ased on a Data Mining.

*uery &anguage 'rchitectures of

Data Mining -ystems

< < +NITIG

Concepts Description.

Characteri!ation and Comparison

Data #enerali!ation and -ummari!ation

ased Characteri!ation

'nalytical Characteri!ation

'nalysis of 'ttri)ute ele(ance

Mining Class Comparisons

Discriminating )et/een Different Classes,

Mining Descripti(e -tatistical Measures in

&arge Data)ases

= = +NITG

Mining 'ssociation ules in &arge

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Data)ases: 'ssociation ule Mining,.

Mining-ingleDimensionaloolean

'ssociation ules from Transactional

Data)ases,

$arehouses, From 'ssociation Mining to

Correlation 'nalysis,

Constraintased 'ssociation Mining

'ssociation Mining

B B +NITGI

Classification and Prediction

Issues egarding Classification and

Prediction

Classification )y Decision Tree Induction,

ayesian Classification

Classification )y ac0 propagation,

Classification ased on Concepts from

'ssociationuleMining,

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%ther Classification Methods, Prediction

Classifier 'ccuracy.

H H +NITGII

Cluster'nalysisIntroduction.

Types of Data in Cluster 'nalysis

' Categori!ation of Major Clustering

Methods

Partitioning Methods

#ridased Methods

Modelased Clustering Methods,

Densityased Methods,

%utlier 'nalysis

+NITGIII

Mining Comple1 Types of Data

Multimensional 'nalysis and Descripti(e

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Mining of Comple1

Data %)jects

Mining-patialData)ases

Mining Multimedia Data)ases

Mining Time-eries and -e2uence

Data, Mining Te1t Data)ases

Mining the $orld $ide $e)

I)?) Sor1e$ o2 I+2orm!"o+

I)?)8) Te@ 5oo

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. Data $arehousing in the eal $orld 4 -'M 'N'"%; 7 D6NNI-

M+';.Pearson6dn'sia.

< Data $arehousing Fundamentals 4 P'+&'5 P%NN'I'" $I&6; -T+D6NT 6DITI%N.

=. The Data $arehouse &ife cy Tool 0it 4 '&P" 8IM'&& $I&6; -T+D6NT 6DITI%N.

.

8)?)>) 3e5$"e$:- H&:''((()*+)!1)"+'

I)?)?) or+!#$:-

8)) U+" ("$e Smm!r7

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-

.no

+

NIT N%

Topic 'dditional

Topics

3 3 Introduction: Fundamentals of data mining, Data

Mining Functionalities

Classification of Data Mining systems,

MajorissuesinDataMining.

Data Preprocessing: Needs Preprocessing the

Data, Data Cleaning,

Data Integration and Transformation, Data

eduction,

*TP

Discreti!ation and

Concept "ierarchy #eneration

9 9 Data $arehouse and %&'P Technology for

DataMiningData$arehouse,

Data $arehouse 'rchitecture

Data$arehouseImplementation

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Further De(elopment of Data Cu)e

Technology

From Data $arehousing to Data Mining.

Multidimensional Data Model

DataMiningPrimiti(es,

Data Mining Primiti(es, Data Mining *uery

&anguages,

Designing #raphical +ser Interfaces

ased on a Data Mining *uery &anguage.

'rchitectures of Data Mining -ystems

and -ystem 'rchitectures

&anguages

< < Concepts Description : Characteri!ation and

Comparison:

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Data #enerali!ation and -ummari!ation &arge

Data)ases

asedCharacteri!ation,'nalytical

Characteri!ation:

-il0 Testing

'nalysis of 'ttri)ute ele(ance, Mining Class

Comparisons

Discriminating )et/een Different Classes,

Mining Descripti(e -tatistical Measures in

= = Mining 'ssociation ules in &arge Data)ases :

'ssociation ule Mining,

Mining -ingleDimensional oolean 'ssociation

ules from Transactional Data)ases,

Mining Multile(el 'ssociation ules from

Transaction Data)ases

Mining Multidimensional 'ssociation ules from

elational Data)ases and Data $arehouses,

From 'ssociation Mining to Correlation 'nalysis

Constraintased'ssociationMining.

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B B Classification and Prediction : Issues egarding

Classification and Prediction,

Classification )y Decision Tree Induction,

ayesian Classification

Classification )y ac0 propagation, 8GC"'T

'PP&IC'TI%N

Classification ased on Concepts from

'ssociation ule Mining

%ther Classification Methods,

Prediction,Classifier'ccuracy.

H H Cluster 'nalysis Introduction : Types of

DatainCluster'nalysis,

' Categori!ation of Major Clustering Methods,

Partitioning Methods,

Densityased Methods, 'utomationTechni2ues

#ridased Methods, Modelased Clustering

Methods, %utlier 'nalysis.

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Mining Comple1 Types of Data :

Multimensional 'nalysis and Descripti(e Mining

of Comple1,

Data %)jects

Mining Time-eries and -e2uence Data, Mining

Te1t Data)ases

Mining the $orld $ide $e).

Mining -patial Data)ases,

Mining Multimedia Data)ases

,,

'gel model

8)) M"1ro P#!+

-

.&

n

+nit

No

Total no of

Periods

Topics to )e co(ered eg>'dditi

onal

Teac

hing aids

used

emar

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o &CD>

%"P>

3 3 3 Introduction : Fundamentals of data mining, egular %"P,

9 Data Mining Functionalities egular %"P,

Classification of Data Mining systems egular %"P,

< MajorissuesinDataMining. egular %"P,

= DataPreprocessing:NeedsPreprocessing the

Data

egular

DataCleaning,DataIntegrationandTransformat

ion

9 9 B Data$arehouseand egular %"P,

H %&'P Technology for Data Mining Data

$arehouse,

egular

MultidimensionalDataModel. egular %"P,

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K Data $arehouse 'rchitecture, egular

3L Data$arehouseImplementation, egular

33 Further De(elopment of Data Cu)e

Technology,

egular %"P,

From Data $arehousing to Data Mining egular

39 DataMiningPrimiti(es egular %"P,

3 Data Mining Primiti(es egular

3< Data Mining *uery &anguages, egular

3= Designing #raphical +ser Interfaces ased

on a Data Mining *uery

egular %"P,

3B &anguage 'rchitectures of Data Mining

-ystems.

egular

&anguages,and-ystem'rchitectures egular %"P,

< < 3H Concepts Description egular

3 Characteri!ation and Comparison egular

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3K Data#enerali!ationand -ummari!ationased

Characteri!ation

egular

9L 'nalytical Characteri!ation: 'nalysis of

'ttri)ute ele(ance

egular

93 MiningClassComparisons:Discriminating

)et/een Different Classes

egular

99 Mining Descripti(e -tatistical Measures in

&arge Data)ases.

= = 9 Mining 'ssociation ules in &arge Data)ases egular

9< 'ssociation ule Mining egular

9= Mining -ingleDimensional oolean

'ssociation ules from Transactional

Data)ases

egular

9B Mining Multile(el 'ssociation ules from

Transaction Data)ases

egular

9H Mining Multidimensional 'ssociation ules

from elational Data)ases and Data

$arehouses

egular

From 'ssociation Mining to Correlation

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'nalysis, Constraintased 'ssociation

Mining.

B B 9 ClassificationandPrediction egular %"P,

9K Issues egarding Classification and

Prediction

egular

L Classification )y Decision Tree Induction egular

3 ayesian Classification egular %"P

9 Classification )y ac0 propagation,

Classification ased on Concepts from

'ssociation ule Mining

egular %"P,

%therClassificationMethods, Prediction,

Classifier 'ccuracy.

egular %"P,

H H Cluster'nalysisIntroduction egular

< Types of Data in Cluster 'nalysis egular

= ' Categori!ation of Major Clustering egular %"P,

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Methods

B Partitioning Methods egular

H Mining Comple1 Types of Data egular %"P,

Multimensional 'nalysis and Descripti(e

Mining of Comple1

egular

K Data %)jects, Mining -patial Data)ases egular &CD,

%"P,

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3.ppts

9.ohp slides

. su)jecti(e type 2uestionsappro1imately = tL in no

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UNIT-I

DEFINITIONS:

DATAMINING:Data mining refers to e1tracting or mining 0no/ledge fromlarge amounts of data)

DATAMINING FUNTIONALITIES:Characteri!ation and discrimination,

Mining Fre2uent Patterns, 'ssociations, and Correlations ,'ssociation 'nalysis,

Classification and Prediction ,Cluster analysis, %utlier analysis, Trend ande(olution analysis

CLASSIFICATION OF DATAMINING SYSTEMS:

Ge+er!# 2+1"o+!#"7

Descripti(e data mining

Predicti(e data mining

D!! m"+"+6 %!r"o$ 1r"er"!$:

8inds of data)ases to )e mined

8inds of 0no/ledge to )e disco(ered

8inds of techni2ues utili!ed

8inds of applications adapted

D!!5!$e$ o 5e m"+ed

elational, transactional, o)jectoriented, o)jectrelational, acti(e, spatial, time

series, te1t, multimedia, heterogeneous, legacy, $$$, etc.

K+o(#ed6e o 5e m"+ed

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Characteri!ation, discrimination, association, classification, clustering, trend,

de(iation and outlier analysis, etc.

Multiple>integrated functions and mining at multiple le(els

analysis, $e) mining, $e)log analysis, etc.

Te14+"e$ "#";ed

Data)aseoriented, data /arehouse %&'P, machine learning, statistics,

(isuali!ation, neural net/or0, etc.

A&"1!"o+$ !d!&ed

etail, telecommunication, )an0ing, fraud analysis, DN' mining, stoc0 mar0et

MAOR ISSUES IN DATAMINING

Mining methodology and user interaction issues

Performance issues

Issues relating to the di(ersity of data types

DATA PREPROSESSING

integrating multiple, heterogeneous data sources

DATA CLEANSING

6nsure consistency in naming con(entions, encoding structures, attri)ute measures,

etc. among different data sources

IT-

3. Re6re$$"o+is the oldest and most /ell0no/n statistical techni2ue that the data mining

community utili!es9. D!! m"+"+6is the use of automated data analysis techni2ues to unco(er pre(iously

undetected relationships among data items

. Three of the major data mining techni2ues are re6re$$"o+, 1#!$$"2"1!"o+ !+d 1#$er"+6)

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H.61plain Preprocess techni2ues@

UNIT-II

DATA3AREHOUSING

' decision support data)ase that is maintained separately from the organi!ationAs

operational data)ase

' data /arehouse is a su)jectoriented, integrated, time(ariant, and non(olatile

collection of data in support of managementAs decisionma0ing process.

DEFINITIONS:

OLAP/o+-#"+e !+!#7"1!# &ro1e$$"+60

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Major tas0 of data /arehouse system

Data analysis and decision ma0ing

MULTIDIMENTIONAL DATAMODEL

-tar schema

-no/fla0e schema

Fact constellations

CUBE DEFINITION /F!1 T!5#e0

define cu)e Ocu)e nameQ ROdimension listQS: Omeasure listQ

DATA3AREHOUSE APPLICATIONS

supports 2uerying, )asic statistical analysis, and reporting using crossta)s, ta)les,

charts and graphs

multidimensional analysis of data /arehouse data

supports )asic %&'P operations, slicedice, drilling, pi(oting

M!*or T!$

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com)ines data from multiple sources into a coherent store

Red+d!+ d!! o11r o2e+ (4e+ "+e6r!"o+ o2 m#"e d!!5!$e$

The same attri)ute may ha(e different names in different data)ases

%ne attri)ute may )e a deri(ed attri)ute in another ta)le, e.g., annual re(enue

edundant data may )e a)le to )e detected )y correlation analysis

Careful integration of the data from multiple sources may help reduce>a(oid

redundancies and inconsistencies and impro(e mining speed and 2uality

D!! red1"o+ $r!e6"e$

Data cu)e aggregation

'ttri)ute su)set selection

Dimensionality reduction

Numerosity reduction

Discreti!ation and concept hierarchy generation

34! "$ T4e #o(e$ #e%e# o2 ! d!! 15e

the aggregated data for an indi(idual entity of interest

e.g., a customer in a phone calling data /arehouse.

P!r!mer"1 me4od$

'ssume the data fits some model, estimate model parameters, store only the

parameters, and discard the data e1cept possi)le outliers

&oglinear models: o)tain (alue at a point in mD space as the product on

appropriate marginal su)spaces

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No+-&!r!mer"1 me4od$

Do not assume models

Major families: histograms, clustering, sampling

D"$1re";!"o+

reduce the num)er of (alues for a gi(en continuous attri)ute )y di(iding the range

of the attri)ute into inter(als. Inter(al la)els can then )e used to replace actual data

(alues.

Co+1e& 4"er!r14"e$

reduce the data )y collecting and replacing lo/ le(el concepts such as numeric

(alues for the attri)ute age )y higher le(el concepts such as young, middleaged,or senior.

IT-

3. A D!! 3!re4o$e Is ' -tructured epository of "istoric Data)

9) A data warehouse integrates data from multiple data sources

>) A data warehouseis a copy of transaction data specifically structured for query and analysis.

?) OLAP stands for On-Line Analytical Processing

) OLAP can be braodly divided into two different ways that is:MOLAP and ROLAP

) ' data /arehouse maintains its functions in three layers $!6"+6, "+e6r!"o+, !+d !11e$$) The data accessed for reporting and analy!ing and the tools for reporting and analy!ing

data is is is also called thed!! m!r)

. D!! !11e$$layer is the interface )et/een the operational and informational access layer) the data /arehousing concept /as intended to pro(ide an architectural model for the flo/

of data from operational systems tode1"$"o+ $&&or e+%"ro+me+$8) The integrationlayer is used to integrate data and to ha(e a le(el of a)straction from

users

E!$7 e$"o+$

3. 61plain Preprocessing procedure@

9. 61plain data Transformation@

. 61plain data Integration@

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UNIT-III

DEFINITIONS

DATAMINING PRIMITIVES

More fle1i)le user interaction

Foundation for design of graphical user interface

-tandardi!ation of data mining industry and practice

DATAMINING UERY LANGUAGES

' DM*& can pro(ide the a)ility to support adhoc and interacti(e data mining

y pro(iding a standardi!ed language li0e -*&

to achie(e a similar effect li0e that -*& has on relational data)ase

Foundation for system de(elopment and e(olution

Facilitate information e1change, technology transfer, commerciali!ation and

/ide acceptance

34! !$

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Data collection and data mining 2uery composition

Presentation of disco(ered patterns

"ierarchy specification and manipulation

Manipulation of data mining primiti(es

Interacti(e multile(el mining

%ther miscellaneous information

34! De2"+e$ ! D!! M"+"+6 T!$< =

Tas0rele(ant data

Type of 0no/ledge to )e mined

ac0ground 0no/ledge

Pattern interestingness measurements

Gisuali!ation of disco(ered patternsT!$

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Mine8no/ledge-pecification ::

mine associations Ras patternnameS

34! !$

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Mine8no/ledge-pecification ::J

mine comparison Ras patternnameS

for targetclass /here targetcondition

V(ersus contrastclassi /here contrastconditioniW

analy!e measures

34! "$ 4e S7+!@ 2or !$D$ systems, efficient

implementation of a fe/ DM primiti(es.

E!$7 e$"o+$

3.61plain Data Mining Primiti(es@

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9.61plain ' data mining 2uery language@

.

'rchitecture of data mining systems@

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Gisuali!ation techni2ues:

Pie charts, )ar charts, cur(es, cu)es, and other (isual forms.

!+"!"%e 14!r!1er"$"1 r#e$

Mapping generali!ed result into characteristic rules /ith 2uantitati(e information

associated /ith it

De1"$"o+ ree

each internal node tests an attri)ute

each )ranch corresponds to attri)ute (alue

each leaf node assigns a classification

ID> !#6or"4m

)uild decision tree )ased on training o)jects /ith 0no/n class la)els to classify

testing o)jects

ran0 attri)utes /ith information gain measure

minimal height

the least num)er of tests to classify an o)ject

De1"$"o+ ree

each internal node tests an attri)ute

each )ranch corresponds to attri)ute (alue

each leaf node assigns a classification

ID> !#6or"4m

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)uild decision tree )ased on training o)jects /ith 0no/n class la)els to classify

testing o)jects

ran0 attri)utes /ith information gain measure

minimal height

the least num)er of tests to classify an o)ject

D!! d"$&er$"o+ 14!r!1er"$"1$

median, ma1, min, 2uantiles, outliers, (ariance, etc.

Nmer"1!# d"me+$"o+$ -1orre$&o+d o $ored "+er%!#$

Data dispersion: analy!ed /ith multiple granularities of precision

o1plot or 2uantile analysis on sorted inter(als

D"$&er$"o+ !+!#7$"$ o+ 1om&ed me!$re$

Folding measures into numerical dimensions

o1plot or 2uantile analysis on the transformed cu)e

!r"#e$, o#"er$ !+d 5o@o$

*uartiles: *39=thpercentile, *H=

thpercentile

Inter2uartile range: I* *4*3

Fi(e num)er summary: min, *3, M,*, ma1

o1plot: ends of the )o1 are the 2uartiles, median is mar0ed, /his0ers, and plot

outlier indi(idually

%utlier: usually, a (alue higher>lo/er than 3.= 1 I*

F"%e-+m5er $mm!r7 o2 ! d"$r"5"o+:

Minimum, *3, M, *, Ma1imum

Bo@o

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Data is represented /ith a )o1

The ends of the )o1 are at the first and third 2uartiles, i.e., the height of the )o1 is

I*

The median is mar0ed )y a line /ithin the )o1

$his0ers: t/o lines outside the )o1 e1tend to Minimum and Ma1imum

S!+d!rd de%"!"o+:

the s2uare root of the (ariance

Measures spread a)out the mean

It is !ero if and only if all the (alues are e2ual

oth the de(iation and the (ariance are alge)raic

D"22ere+1e "+ &4"#o$o&4"e$ !+d 5!$"1 !$$m&"o+$

Positi(e and negati(e samples in learningfrome1ample: positi(e used for

generali!ation, negati(e for speciali!ation

Positi(e samples only in data mining: hence generali!ation)ased, to drilldo/n

)ac0trac0 the generali!ation to a pre(ious state

D"22ere+1e "+ me4od$ o2 6e+er!#";!"o+$

Machine learning generali!es on a tuple )y tuple )asis

Data mining generali!es on an attri)ute )y attri)ute )asis

IT-

8) C4!r!1er";!"o+ of the composition of the postsynaptic proteome P-P pro(ides a

frame/or0 for understanding the o(erall organi!ation and function9) Clustering using representati(es called CURE

>) The D!! M"+"+6 Ser%ermust )e integrated /ith the data /arehouse and the %&'Pser(er to em)ed %Ifocused )usiness analysis directly into this infrastructure

?) ' de1"$"o+ ree techni2ue used for classification of a dataset

) 1#!$$"2"1!"o+ The process of di(iding a dataset into mutually e1clusi(e groups

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) d!! 1#e!+$"+6is The process of ensuring that all (alues in a dataset are consistent andcorrectly recorded.

) d!! (!re4o$e is a system for storing and deli(ering massi(e 2uantities of data.

) !+!#7"1!# mode#is a structure and process for analy!ing a dataset

) d!! +!%"6!"o+The process of (ie/ing different dimensions, slices, and le(els of detailof a multidimensional data)ase.

8) #o6"$"1 re6re$$"o+a linear regression that predicts the proportions of a categorical target(aria)le, such as type of customer, in a population.

6asy *uestions

3. 61plain $hat is concept description@

9. Data generali!ation and summari!ation)ased characteri!ation@

. 'nalytical characteri!ation: 'nalysis of attri)ute rele(ance@

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UNIT-V

A$$o1"!"o+ r#e m"+"+6

Finding fre2uent patterns, associations, correlations, or causal structures

among sets of items or o)jects in transaction data)ases, relational

data)ases, and other information repositories.

B!$"1 Co+1e&$ o2 A$$o1"!"o+ R#e

#i(en a data)ase of transactions each transaction is a list of items purchased )y a

customer in a (isit

Find all rules that correlate the presence of one set of items /ith that of another set

of items

Find fre2uent patterns

61ample for fre2uent itemset mining is mar0et )as0et analysis.

A$$o1"!"o+ r#e &er2orm!+1e me!$re$

Confidence

-upport

Minimum support threshold

Minimum confidence threshold

M!r

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Identify patterns from oolean (ector

Patterns can )e represented )y association rules.

A&r"or" A#6or"4m

-ingle dimensional, singlele(el, oolean fre2uent item sets

Finding fre2uent item sets using candidate generation

#enerating association rules from fre2uent item sets

S"+6#e-d"me+$"o+!# r#e$

)uysJ, mil0 )uysJ, )read

M#"-d"me+$"o+!# r#e$

Interdimension association rules no repeated predicates

ageJ,3K9= occupationJ,student )uysJ,co0e

hy)riddimension association rules repeated predicates

ageJ,3K9= )uysJ, popcorn )uysJ, co0e

C!e6or"1!# Ar"5e$

finite num)er of possi)le (alues, no ordering among (alues

!+"!"%e Ar"5e$

numeric, implicit ordering among (alues

S!"1 D"$1re";!"o+ o2 !+"!"%e Ar"5e$

Discreti!ed prior to mining using concept hierarchy.

Numeric (alues are replaced )y ranges.

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In relational data)ase, finding all fre2uent 0predicate sets /ill re2uire k

or kX3 ta)le scans.

Data cu)e is /ell suited for mining.

The cells of an ndimensional cu)oid correspond to the predicate sets.

Mining from data cu)escan )e much faster.

O5*e1"%e me!$re$

T/o popular measurements

support

confidence

S5*e1"%e me!$re$

' rule pattern is interesting if

Yit is unexpectedsurprising to the user and>or

*actionablethe user can do something /ith it

le(el constraints

ule constraint

Interestingness constraints

A 1o+$r!"+ C!"$ !+"-mo+oo+e "22) 2or !+7 &!er+ S +o $!"$27"+6

C!, +o+e o2 4e $&er-&!er+$ o2 S 1!+ $!"$27 C!

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A 1o+$r!"+ Cm"$ mo+oo+e "22) 2or !+7 &!er+ S $!"$27"+6 Cm,

e%er7 $&er-&!er+ o2 S !#$o $!"$2"e$ "

S11"+1+e$$ Pro&er7 o2 Co+$r!"+$

For any set -3 and -9 satisfying C, -3 -9 satisfies C

#i(en '3 is the sets of si!e 3 satisfying C, then any set - satisfying C are )ased on

'3 , i.e., it contains a su)set )elongs to '3 ,

61ample :

sum(S.Price ) v is not succinct

min(S.Price ) v is succinct

IT-

3. A+ !$$o1"!"o+rule is a pattern that states /henXoccurs, occurs /ith certain

pro)a)ility9. Go!#Find all rules that satisfy the userspecified minimum support minsup and

minimum confidenceminconf.. T!5#e d!!need to )e con(erted to transaction form for association mining.

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6asy *uestions

3.e1plain

'ssociation rule mining@

9.

Mining singledimensional oolean association rules from transactional

data)ases@

.61plain Mining multile(el association rules from transactional data)ases@

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UNIT-VI

C#!$$"2"1!"o+:predicts categorical class la)els

classifies data constructs a model )ased on the training set and the (alues class

la)els in a classifying attri)ute and uses it in classifying ne/ data

Pred"1"o+:

models continuous(alued functions

predicts un0no/n or missing (alues

S&er%"$ed #e!r+"+6 /1#!$$"2"1!"o+0

-uper(ision: The training data o)ser(ations, measurements, etc. are accompanied

)y la)els indicating the class of the o)ser(ations

Ne/ data is classified )ased on the training set

U+$&er%"$ed #e!r+"+6 /1#$er"+60

The class la)els of training data is un0no/n

#i(en a set of measurements, o)ser(ations, etc. /ith the aim of esta)lishing the

e1istence of classes or clusters in the data

I$$e$ re6!rd"+6 1#!$$"2"1!"o+ !+d &red"1"o+ Com&!r"+6 C#!$$"2"1!"o+

Me4od$

'ccuracy

-peed and scala)ility

o)ustness

-cala)ility

Interpreta)ility:

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Interpreta)ility

De1"$"o+ ree

' flo/chartli0e tree structure

Internal node denotes a test on an attri)ute

ranch represents an outcome of the test

&eaf nodes represent class la)els or class distri)ution

De1"$"o+ ree 6e+er!"o+ 1o+$"$$ o2 (o &4!$e$

Tree construction

't start, all the training e1amples are at the root

Partition e1amples recursi(ely )ased on selected attri)utes

Tree pruning

Identify and remo(e )ranches that reflect noise or outliers

U$e o2 de1"$"o+ ree: C#!$$"27"+6 !+ +

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E@r!1"+6 C#!$$"2"1!"o+ R#e$ 2rom Tree$

epresent the 0no/ledge in the form of IFT"6N rules

%ne rule is created for each path from the root to a leaf

6ach attri)ute(alue pair along a path forms a conjunction

The leaf node holds the class prediction

ules are easier for humans to understand

T(o !&&ro!14e$ o !%o"d o%er2""+6

Pre&r+"+6:"alt tree construction earlyUdo not split a node if this /ould result

in the goodness measure falling )elo/ a threshold

Difficult to choose an appropriate threshold

Po$&r+"+6: emo(e )ranches from a fully gro/n treeUget a se2uence of

progressi(ely pruned trees

+se a set of data different from the training data to decide /hich is the )est

pruned tree

A&&ro!14e$ o Deerm"+e 4e F"+!# Tree S";e

-eparate training and testing sets

+se cross (alidation, 3Lfold cross (alidation

+se all the data for training

+se minimum description length MD& principle

E+4!+1eme+$ o 5!$"1 de1"$"o+ ree "+d1"o+

'llo/ for continuous(alued attri)utes

"andle missing attri)ute (alues

'ttri)ute construction

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C#!$$"2"1!"o+Ua classical pro)lem e1tensi(ely studied )y statisticians and

machine learning researchers

S1!#!5"#"7:Classifying data sets /ith millions of e1amples and hundreds of

attri)utes /ith reasona)le speed

347 de1"$"o+ ree "+d1"o+ "+ d!! m"+"+6=

relati(ely faster learning speed than other classification methods

con(erti)le to simple and easy to understand classification rules

can use -*& 2ueries for accessing data)ases

compara)le classification accuracy /ith other methods

B!7e$"!+ C#!$$"2"1!"o+

-tatical classifiers

ased on ayeAs theorem

NaZ(e ayesian classification

Class conditional independence

B!7e$"!+ 5e#"e2 +e(o

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E@r!1"+6 r#e$ 2rom ! r!"+ed +e(or

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Major method for prediction is regression

&inear and multiple regressions

Nonlinear regression

L"+e!r re6re$$"o+:; X J

T/o parameters, and specify the line and are to )e estimated )y using the data

at hand.

+sing the least s2uares criterion to the 0no/n (alues of ;3, ;9[ J3, J9, [.

M#"e re6re$$"o+:; )L X )3 J3 X )9 J9.

Many nonlinear functions can )e transformed into the a)o(e.

Lo6-#"+e!r mode#$:

The multi/ay ta)le of joint pro)a)ilities is appro1imated )y a product of lo/er

order ta)les.

Pro5!5"#"7:p(a! b! c! d) " ab acad bcd

3. Mode# 1o+$r1"o+descri)ing a set of predetermined classes.

9. S1!#!5"#"7 Classifying data sets /ith millions of e1amples and hundreds of attri)utes

/ith reasona)le speed. C#!$$"2"1!"o+predicts categorical class la)els.

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9. 61plain IssuesA regarding classification and prediction@

. 61plainClassification )y decision tree induction@

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Inter(alscaled (aria)les

inary (aria)les

Categorical, %rdinal, and atio -caled (aria)les

Garia)les of mi1ed types

M!*or C#$er"+6 A&&ro!14e$

Partitioning algorithms

"ierarchy algorithms

Density)ased

#rid)ased

Model ased

%utlier 'nalysis

CLARA/C#$er"+6 L!r6e A&"1!"o+$0 /80

#$%&%8aufmann and ousseeu/ in 3KKL

uilt in statistical analysis pac0ages, such as -XIt dra/s multiple samplesof the data set, appliesP%'on each sample, and gi(es

the )est clustering as the output

-trength: deals /ith larger data sets thanP%'

$ea0ness:

6fficiency depends on the sample si!e

' good clustering )ased on samples /ill not necessarily represent a goodclustering of the /hole data set if the sample is )iased

B"r14:alanced Iterati(e educing and Clustering using "ierarchies, )y \hang,

ama0rishna, &i(ny -I#M%DAKB

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CHAMELEON: hierarchical clustering using dynamic modeling, )y #. 8arypis,

6.". "an and G. 8umarAKK

DBSCAN A#6or"4m

'r)itrary select a pointp

etrie(e all points densityreacha)le fromp/rtpsand'inPts.

Ifpis a core point, a cluster is formed.

Ifpis a )order point, no points are densityreacha)le frompand D-C'N (isits

the ne1t point of the data)ase.

Continue the process until all of the points ha(e )een processed.

L"m"!"o+$ o2 COB3EB

The assumption that the attri)utes are independent of each other is often too strong

)ecause correlation may e1ist

Not suita)le for clustering large data)ase data 4 s0e/ed tree and e1pensi(e

pro)a)ility distri)utions

Ner!# +e(or< !&&ro!14e$

epresent each cluster as an e1emplar, acting as a prototype of the cluster

Ne/ o)jects are distri)uted to the cluster /hose e1emplar is the most similar

according to some do stance measure

O#"er$

The set of o)jects are considera)ly dissimilar from the remainder of the data

61ample: -ports: Michael 5ordon, $ayne #ret!0y, ...

D"$!+1e-5!$ed o#"er:' D p, Doutlier is an o)ject % in a dataset T such that

at least a fraction p of the o)jects in T lies at a distance greater than D from %

A#6or"4m$ 2or m"+"+6 d"$!+1e-5!$ed o#"er$

Inde1)ased algorithm

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Nestedloop algorithm

Cell)ased algorithm

See+"!# e@1e&"o+ e14+"e

-imulates the /ay in /hich humans can distinguish unusual o)jects from among a

series of supposedly li0e o)jects

OLAP d!! 15e e14+"e

+ses data cu)es to identify regions of anomalies in large multidimensional data

IT-

8) 1#$er"+6is the assignment of a set of o)ser(ations into su)sets.9) S5$&!1e 1#$er"+6methods loo0 for clusters that can only )e seen in a particular

projection of the data.>) Many clustering algorithms re2uire the $&e1"2"1!"o+ o2 4e +m5er o2 1#$er$to

produce in the input data set, prior to e1ecution of the algorithm.?) D"$!+1e me!$re/hich /ill determine ho/ thesimilarityof t/o elements is calculated.

) "ierarchical clustering creates a 4"er!r147of clusters /hich may )e represented in a ree

$r1recalled a dendrogram.

) T 1#$er"+6 "s an alternati(e method of partitioning data, in(ented for gene clustering.

H. *T clustering *T stands for !#"7 T4re$4o#d.) Form!# 1o+1e& !+!#7$"$is a techni2ue for generating clusterscalled formal concepts

of o)jects and attri)utes.) 6(aluation of clustering is sometimes referred to as C#$er %!#"d!"o+.

Several dierent clustering systems based on mutua5 %&fomat%o&have been

proposed.

E!$7 e$"o+

3.

hat is Cluster !nalysis"

9. 61plain Types of Data in Cluster 'nalysis@

. 61plain ' Categori!ation of Major Clustering Methods@

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H. 61plain #ridased Methods@

. 61plain Modelased Clustering Methods@

K. 61plain %utlier 'nalysis@

UNIT-VIII

Se-%!#ed !r"5e

#enerali!ation of each (alue in the set into its corresponding higherle(el concepts

Deri(ation of the general )eha(ior of the set, such as the num)er of elements in the

set, the types or (alue ranges in the set, or the /eighted a(erage for numerical data

hobby Vtennis! hockey! chess! violin! nintendogamesW generali!es to Vsports!music! videogamesW

L"$-%!#ed or ! $ee+1e-%!#ed !r"5e

-ame as set(alued attri)utes e1cept that the order of the elements in the se2uence

should )e o)ser(ed in the generali!ation

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S&!"!# d!!:

#enerali!e detailed geographic points into clustered regions, such as )usiness,

residential, industrial, or agricultural areas, according to land usage

e2uire the merge of a set of geographic areas )y spatial operations

Im!6e d!!:

61tracted )y aggregation and>or appro1imation

-i!e, color, shape, te1ture, orientation, and relati(e positions and structures of the

contained o)jects or regions in the image

M$"1 d!!:

-ummari!e its melody: )ased on the appro1imate patterns that repeatedly occur in

the segment

Smm!r";ed "$ $7#e: )ased on its tone, tempo, or the major musical instruments

played

O5*e1 "de+"2"er:generali!e to the lo/est le(el of class in the class>su)class

hierarchies

C#!$$ 1om&o$""o+ 4"er!r14"e$

generali!e nested structured data

generali!e only o)jects closely related in semantics to the current one

P#!+:a (aria)le se2uence of actions

6.g., Tra(el flight: Otra(eler, departure, arri(al, dtime, atime, airline, price,

seatQ

P#!+ m"+"+6:e1traction of important or significant generali!ed se2uential

patterns from a plan)ase a large collection of plans

6.g., Disco(er tra(el patterns in an air flight data)ase, or

find significant patterns from the se2uences of actions in the repair of automo)iles

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S&!"!# d!! (!re4o$e: Integrated, su)jectoriented, time(ariant, and

non(olatile spatial data repository for data analysis and decision ma0ing

S&!"!# d!! "+e6r!"o+:a )ig issue

-tructurespecific formats raster (s. (ector)ased, %% (s. relational models,

different storage and inde1ing

Gendorspecific formats 6-I, MapInfo, Integraph

S&!"!# d!! 15e:multidimensional spatial data)ase

oth dimensions and measures may contain spatial components

S&!"!# !$$o1"!"o+ r#e:%Rs], c]S

' and are sets of spatial or nonspatial predicates

To&o#o6"1!# re#!"o+$:intersects! overlaps! dis+oint! etc.

S&!"!# or"e+!"o+$:leftof! ,estof! under!etc.

D"$!+1e "+2orm!"o+:closeto! ,ithindistance! etc.

H"er!r147 o2 $&!"!# re#!"o+$4"&:

gcloseto: nearby,touch,intersect, contain, etc.

First search for rough relationship and then refine it

S&!"!# 1#!$$"2"1!"o+

'naly!e spatial o)jects to deri(e classification schemes, such as decision trees in

rele(ance to certain spatial properties district, high/ay, ri(er, etc.

61ample: Classify regions in a pro(ince into rich(s.pooraccording to the a(erage

family income

De$1r"&"o+-5!$ed rer"e%!# $7$em$

uild indices and perform o)ject retrie(al )ased on image descriptions, such as

0ey/ords, captions, si!e, and time of creation

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&a)orintensi(e if performed manually

esults are typically of poor 2uality if automated

Co+e+-5!$ed rer"e%!# $7$em$

-upport retrie(al )ased on the image content, such as color histogram, te1ture,

shape, o)jects, and /a(elet transforms

Im!6e $!me-5!$ed er"e$:

Find all of the images that are similar to the gi(en image sample

Compare the feature (ector signature e1tracted from the sample /ith the feature

(ectors of images that ha(e already )een e1tracted and inde1ed in the image

data)ase

Im!6e 2e!re $&e1"2"1!"o+ er"e$:

-pecify or s0etch image features li0e color, te1ture, or shape, /hich are translated

into a feature (ector

Match the feature (ector /ith the feature (ectors of the images in the data)ase

T"me-$er"e$ d!!5!$e

Consists of se2uences of (alues or e(ents changing /ith time

Data is recorded at regular inter(als

Characteristic timeseries components

Trend, cycle, seasonal, irregular

E$"m!"o+ o2 171#"1 %!r"!"o+$

If appro1imate periodicity of cycles occurs, cyclic inde1 can )e constructed inmuch the same manner as seasonal inde1es

E$"m!"o+ o2 "rre6#!r %!r"!"o+$

y adjusting the data for trend, seasonal and cyclic (ariations

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Se&$ 2or &er2orm"+6 ! $"m"#!r"7 $e!r14

Aom"1 m!14"+6

Find all pairs of gapfree /indo/s of a small length that are similar

3"+do( $"14"+6

-titch similar /indo/s to form pairs of large similar su)se2uences allo/ing gaps

)et/een atomic matches

S5$ee+1e Order"+6

&inearly order the su)se2uence matches to determine /hether enough similar

pieces e1ist

Pro5#em$ ("4 4e 3e5 #"+

Not e(ery hyperlin0 represents an endorsement

%ther purposes are for na(igation or for paid ad(ertisements

If the majority of hyperlin0s are for endorsement, the collecti(e opinion /ill still

dominate

%ne authority /ill seldom ha(e its $e) page point to its ri(al authorities in the

same field

'uthoritati(e pages are seldom particularly descripti(e

H5

-et of $e) pages that pro(ides collections of lin0s to authorities

HITS /H7&er#"+

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De$"6+ o2 ! 3e5 Lo6 M"+er

$e) log is filtered to generate a relational data)ase

' data cu)e is generated form data)ase

%&'P is used to drilldo/n and rollup in the cu)e

%&'M is used for mining interesting 0no/ledge

Be+e2"$ o2 M#"-L!7er Me!-3e5

Multidimensional $e) info summary analysis

'ppro1imate and intelligent 2uery ans/ering

$e) highle(el 2uery ans/ering $e)-*&, $e)M&

$e) content and structure mining

%)ser(ing the dynamics>e(olution of the $e)

IT-

3. A "me Ser"e$ D!!5!$econsists of se2uences of (alues or e(ents o)tained o(er repeated

measurements of time.9. See+"!# P!er+ M"+"+6 is the disco(ery of fre2uently occurring ordered e(ents as

patterns.. D-M- stands forD!! Sre!m M!+!6eme+ S7$em)

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3.

#ultidimensional analysis and descriptive mining of comple$

data objects

9. 61plain mining spatial data)ases

. 61plain

#ultidimensional analysis and descriptive mining of

comple$ data objects"

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