01. introductionoverview of data mining

8/20/2019 01. IntroductionOverview of Data Mining

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Introduction of Data Mining

Session 01

Matakuliah : M0824-Data Mining Tahun : Sep - 2011


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Bina NusantaraUni ersit! "

Learning Outcomes#$plain data %ining concepts and techni&ues'


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Acknowledgments

These slides have been adaptedfrom Han, J., Kamber, ., !

"ei, #. $%&&'(. )ata ining*+oncepts and Techni ue. -disi%. organ Kaufman. an/rancisco.


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( 0hat otivated )ata ining1( )ata ining 2 On 0hat Kind of )ata1

( )ata ining /unctionalities( +lassi3cation of )ata ining 4stems( 5ntegration of a )ata ining 4stems with a

)atabase or )ata 0arehouse 4stems

( a6or 5ssues in )ata ining

Outline ateri

5


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0hat otivated )ata

ining1

Bina NusantaraUni ersit! )


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0h4 )ata ining1( The #$plosi e *ro+th of Data: fro% tera,!tes to peta,!tes

Data collection and data a aila,ilit!

( .uto%ated data collection tools/ data,ase s!ste%s/ e,/ co%puteri edsociet!

Ma or sources of a,undant data

( Business: e,/ e-co%%erce/ transactions/ stocks/ 3

( Science: 4e%ote sensing/ ,ioinfor%atics/ scienti5c si%ulation/ 3

( Societ! and e er!one: ne+s/ digital ca%eras/ 6ouTu,e

( e are dro+ning in data/ ,ut star ing for kno+ledge7

( 8Necessit! is the %other of in ention9 Data %ining .uto%ated anal!sis of %assi edata sets

Bina Nusantara Uni ersit!

;


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0hat 5s )ata ining1( Data %ining


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March )/ 201)

Data Mining: oncepts and Techni&ues C

?no+ledge Disco er!


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0hat is $not( )ata ining1 What is Data Mining?

– Certain names are more prevalent incertain US locations ( !"rien# !$%r&e#!$eill' in "oston area)

– *ro%p together similar +oc%mentsret%rne+ ,' search engine accor+ing to

their conte t (e.g. /ma on rain1orest# /ma on.com#)

What is not Data Mining?

– oo& %p phone

n%m,er in phone+irector'

– 3%er' a We, searchengine 1or in1ormation

a,o%t /ma on


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March )/ 201)

Data Mining: oncepts and Techni&ues 11

Data Mining in Business Intelligence Increasing potentialto supportbusiness decisions End User

Business nal!st

Datanal!st

DB

Decision Making

Data PresentationVisualization Techniques

Data Mining Information Discovery

Data Exploration Statistical Summary, Querying, and Reporting

Data Preprocessing/Integration, Data Warehouses

Data Sources Paper, iles, !e" documents, Scientific e#periments, Data"ase Systems


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Data Mining Tasks'''( lassi5cation E redicti eF( lustering EDescripti eF( .ssociation 4ule Disco er! EDescripti eF

( Se&uential attern Disco er! EDescripti eF( 4egression E redicti eF( De iation Detection E redicti eF


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)ata ining 2 On 0hat

Kind of )ata1

Bina NusantaraUni ersit! 1"


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March )/ 201)

Data Mining: oncepts and Techni&ues 1G

Data Mining Hunction:


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Data Mining: oncepts and Techni&ues 1K


( o+ to use such patterns for classi5cation/ clustering/and other applications>


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.ssociation 4ule Disco er!: .pplication

1( Marketing and Sales ro%otion:Jet the rule disco ered ,e {Bagels, … } --> {Potato Chips}

otato hips as conse&uent OP an ,e used to

deter%ine +hat should ,e done to ,oost its sales'Bagels in the antecedent OP an ,e used to see +hichproducts +ould ,e aQected if the store discontinuesselling ,agels'Bagels in antecedent and otato chips in conse&uent OP

an ,e used to see +hat products should ,e sold +ithBagels to pro%ote sale of otato chips7


1)


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.ssociation 4ule Disco er!: .pplication

2( Super%arket shelf %anage%ent'*oal: To identif! ite%s that are ,ought together ,!su cientl! %an! custo%ers'.pproach: rocess the point-of-sale data collected +ith,arcode scanners to 5nd dependencies a%ong ite%s'. classic rule --

( If a custo%er ,u!s diaper and %ilk/ then heis er! likel! to ,u! ,eer'


1;


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March )/ 201)

Data Mining: oncepts and Techni&ues 1A



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lassi5cation: .pplication 1( Direct Marketing

*oal: 4educe cost of %ailing ,! targeting a setof consu%ers likel! to ,u! a ne+ cell-phoneproduct'.pproach:

( Use the data for a si%ilar product introduced ,efore'( e kno+ +hich custo%ers decided to ,u! and +hich

decided other+ise' This {buy, don’t buy} decisionfor%s the class attribute '


1C


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lassi5cation: .pplication 2

( Hraud Detection*oal: redict fraudulent cases in credit cardtransactions'.pproach:

( Use credit card transactions and the infor%ation onits account-holder as attri,utes'

hen does a custo%er ,u!/ +hat does he ,u!/ ho+often he pa!s on ti%e/ etc


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lassi5cation: .pplication "( usto%er .ttrition@ hurn:

*oal: To predict +hether a custo%er is likel! to ,e lost to aco%petitor'.pproach:

( Use detailed record of transactions +ith eachof the past and present custo%ers/ to 5ndattri,utes'

o+ often the custo%er calls/ +here he calls/ +hat ti%e-of-the da!he calls %ost/ his 5nancial status/ %arital status/ etc'

( Ja,el the custo%ers as lo!al or dislo!al'


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Data Mining: oncepts and Techni&ues 22



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lustering: .pplication 1( Market Seg%entation:

*oal: su,di ide a %arket into distinct su,setsof custo%ers +here an! su,set %a!concei a,l! ,e selected as a %arket target to,e reached +ith a distinct %arketing %i$'.pproach:

( ollect diQerent attri,utes of custo%ers ,ased ontheir geographical and lifest!le related infor%ation'

( Hind clusters of si%ilar custo%ers'


2"


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lustering: .pplication 2( Docu%ent lustering:

*oal: To 5nd groups of docu%ents that are si%ilar toeach other ,ased on the i%portant ter%s appearing inthe%'

.pproach: To identif! fre&uentl! occurring ter%s in eachdocu%ent' Hor% a si%ilarit! %easure ,ased on thefre&uencies of diQerent ter%s'


2G


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Data Mining: oncepts and Techni&ues 2)

Data Mining: on uence of Multiple Disciplines

Data Mining

Machineearning

Statistics

/pplications

/lgorithm

6attern$ecognition

7igh-6er1ormanceComp%ting

is%ali ation

Data,ase9echnolog'


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5ntegration of a )ata ining4stems with a )atabase or

)ata 0arehouse 4stems

Bina NusantaraUni ersit! 2;


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March )/ 201)

Data Mining: oncepts and Techni&ues 2A

Integration of Data Mining and Data arehousing

( )ata mining s4stems, )7 , )ata warehouse s4stems coupling

No coupling/ loose-coupling/ se%i-tight-coupling/ tight-coupling

( On8line anal4tical mining data

integration of %ining and J. technologies

( 5nteractive mining multi8level knowledge

Necessit! of %ining kno+ledge and patterns at diQerent le els of

a,straction ,! drilling@rolling/ pi oting/ slicing@dicing/ etc'( 5ntegration of multiple mining functions

haracteri ed classi5cation/ 5rst clustering and then association


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March )/ 201)

Data Mining: oncepts and Techni&ues 2C

.rchitecture: T!pical Data Mining S!ste%

data cleaning" integration" and selection

Data,ase or DataWareho%se Server

Data Mining :ngine

6attern :val%ation

*raphical User ;nter1ace


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a6or 5ssues in )ata

ining

Bina NusantaraUni ersit! "0


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March )/ 201)

Data Mining: oncepts and Techni&ues "1

Ma or hallenges in Data Mining( # cienc! and scala,ilit! of data %ining algorith%s

( arallel/ distri,uted/ strea%/ and incre%ental %ining %ethods

( andling high-di%ensionalit!

( andling noise/ uncertaint!/ and inco%pleteness of data

( Incorporation of constraints/ e$pert kno+ledge/ and ,ackgroundkno+ledge in data %ining

( attern e aluation and kno+ledge integration

( Mining di erse and heterogeneous kinds of data: e'g'/

,ioinfor%atics/ e,/ soft+are@s!ste% engineering/ infor%ationnet+orks

( .pplication-oriented and do%ain-speci5c data %ining

( In isi,le data %ining


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01. introductionoverview of data mining

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