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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1

Data Mining: Introduction

Lecture Note !or "ha#ter 1

Introduction to Data Miningb$

Tan, Steinbach, Kumar


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Lot o! data i being co%%ected

and &arehoued

' (eb data, e)commerce

' #urchae at de#artment/

grocer$ tore

' *an+/"redit "ard

tranaction

"om#uter hae become chea#er and more #o&er!u%

"om#etitie -reure i Strong

' -roide better, cutomi.ed erice !or an edge eg in

"utomer e%ationhi# Management

Why Mine Data? CommercialViewpoint


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Why Mine Data? Scientifc Viewpoint

Data co%%ected and tored at

enormou #eed 3*/hour

' remote enor on a ate%%ite

' te%eco#e canning the +ie

' microarra$ generating gene

e#reion data

' cienti!ic imu%ation

generating terab$te o! data Traditiona% techni5ue in!eaib%e !or ra& data

Data mining ma$ he%# cientit

' in c%ai!$ing and egmenting data

' in 6$#othei 7ormation


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Mining Large Data Sets - Motiation

There is often information !hidden" in the data that is

not readily evident Human analysts may take weeks to discover useful

information

Much of the data is never analyzed at all

0

00,000

1,000,000

1,00,000

2,000,000

2,00,000

9,000,000

9,00,000

4,000,000

1:: 1::; 1::< 1::8 1:::

The Data Gap

Total new disk (TB) since 1995

Number of

analysts

7rom= 3roman, " Kamath, > Kumar, ?Data Mining !or Scienti!ic and @ngineering A##%icationB


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004

What is Data Mining?

Many Definitions ' Non-trivial extraction of implicit, previously

unknown and potentially useful information fromdata

' Exploration analysis, !y automatic orsemi-automatic means, oflar"e #uantities of datain order to discovermeanin"ful patterns


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ;

What is #not$ Data Mining?

$hat is Data Minin"%

' "ertain name are more#rea%ent in certain CS%ocation E*rien, Eur+e,Eei%%$F in *oton area

' 3rou# together imi%ar

document returned b$earch engine according totheir contet eg Ama.onrain!oret, Ama.oncom,

$hat is not DataMinin"%

' Loo+ u# #hone

number in #honedirector$

' Guer$ a (eb

earch engine !orin!ormation about?Ama.onB


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 <

Draws ideas from machine learnin"&'(, pattern

reco"nition, statistics, and data!ase systems

Traditional Techni#ues

may !e unsuita!le due to

' Enormity of data ' Hi"h dimensionality

of data

' Hetero"eneous,distri!uted nature

of data

%rigins o& Data Mining

Machine Learning/

-attern

ecognition

Statitic/

AI

Data Minin"

Databae

$tem


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Data Mining 'as(s

-rediction Method ' Ce ome ariab%e to #redict un+no&n or

!uture a%ue o! other ariab%e

Decri#tion Method

' 7ind human)inter#retab%e #attern that

decribe the data

rom !ayyad" et#al#$ %d&ances in 'nowlede Disco&ery and Data inin" 199*


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 :

Data Mining 'as(s)))

"%ai!ication H-redictie

"%utering HDecri#tie

Aociation u%e Dicoer$ HDecri#tie

Se5uentia% -attern Dicoer$ HDecri#tie

egreion H-redictie

Deiation Detection H-redictie


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Classifcation: Defnition

3ien a co%%ection o! record training set ' @ach record contain a et o! attributes, one o! the

attribute i the class

7ind a model !or c%a attribute a a !unction

o! the a%ue o! other attribute 3oa%= #reiou%$ uneen record hou%d be

aigned a c%a a accurate%$ a #oib%e ' A test set i ued to determine the accurac$ o! the

mode% Cua%%$, the gien data et i diided intotraining and tet et, &ith training et ued to bui%dthe mode% and tet et ued to a%idate it


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Classifcation *+ample

Tid )efund Marital

*tatus

Taxa!le

(ncome +heat

1 Je Sing%e 12K No

2 No Married 100K No

9 No Sing%e


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Classifcation: ,pplication

Direct Mar+eting ' 3oa%= educe cot o! mai%ing b$ targeting a et o!

conumer %i+e%$ to bu$ a ne& ce%%)#hone #roduct

' A##roach=

Ce the data !or a imi%ar #roduct introduced be!ore(e +no& &hich cutomer decided to bu$ and &hich

decided other&ie Thi {buy, don’t buy} deciion !orm the

class attribute

"o%%ect ariou demogra#hic, %i!et$%e, and com#an$)

interaction re%ated in!ormation about a%% uch cutomer

+ Type of business" where they stay" how much they earn" etc#

Ce thi in!ormation a in#ut attribute to %earn a c%ai!ier

mode%rom !Berry , -inoff$ Data inin Techni.ues" 199/


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Classifcation: ,pplication .

7raud Detection ' 3oa%= -redict !raudu%ent cae in credit card

tranaction

' A##roach=Ce credit card tranaction and the in!ormation on it account)

ho%der a attribute + 0hen does a customer buy" what does he buy" how often he pays on time"

etc Labe% #at tranaction a !raud or !air tranaction Thi !orm

the c%a attribute

Learn a mode% !or the c%a o! the tranaction

Ce thi mode% to detect !raud b$ obering credit cardtranaction on an account


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Classifcation: ,pplication /

"utomer Attrition/"hurn=

' 3oa%= To #redict &hether a cutomer i %i+e%$ to

be %ot to a com#etitor

' A##roach=Ce detai%ed record o! tranaction &ith each o! the

#at and #reent cutomer, to !ind attribute

+ ow often the customer calls" where he calls" what time2of2the

day he calls most" his financial status" marital status" etc#

Labe% the cutomer a %o$a% or di%o$a%7ind a mode% !or %o$a%t$

rom !Berry , -inoff$ Data inin Techni.ues" 199/


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Classifcation: ,pplication 0

S+$ Sure$ "ata%oging

' 3oa%= To #redict c%a tar or ga%a$ o! +$ obect,

e#ecia%%$ iua%%$ !aint one, baed on the te%eco#ic

ure$ image !rom -a%omar berator$

+ 3444 imaes with 3"464 7 3"464 pi7els per imae#

' A##roach=Segment the image

Meaure image attribute !eature ) 40 o! them #er obect

Mode% the c%a baed on thee !eature

Succe Stor$= "ou%d !ind 1; ne& high red)hi!t 5uaar,

ome o! the !arthet obect that are di!!icu%t to !ind

rom !ayyad" et#al#$ %d&ances in 'nowlede Disco&ery and Data inin" 199*


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1;

Clustering Defnition

3ien a et o! data #oint, each haing a et o!attribute, and a imi%arit$ meaure among them,!ind c%uter uch that

' Data #oint in one c%uter are more imi%ar to

one another ' Data #oint in e#arate c%uter are %eimi%ar to one another

Simi%arit$ Meaure=

' @uc%idean Ditance i! attribute arecontinuou

' ther -rob%em)#eci!ic Meaure


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1<

Illustrating Clustering

Euclidean Distance Based Clustering in 3-D space.

8ntracluster distances

are minimied

8ntracluster distances

are minimied8ntercluster distances

are ma7imied

8ntercluster distances

are ma7imied


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Clustering: ,pplication

Mar+et Segmentation=

' 3oa%= ubdiide a mar+et into ditinct ubet o!cutomer &here an$ ubet ma$ conceiab%$ bee%ected a a mar+et target to be reached &ith aditinct mar+eting mi

' A##roach="o%%ect di!!erent attribute o! cutomer baed on their

geogra#hica% and %i!et$%e re%ated in!ormation

7ind c%uter o! imi%ar cutomer

Meaure the c%utering 5ua%it$ b$ obering bu$ing #attern

o! cutomer in ame c%uter thoe !rom di!!erent c%uter


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1:

Clustering: ,pplication .

Document "%utering=

' 3oa%= To !ind grou# o! document that are

imi%ar to each other baed on the im#ortant

term a##earing in them

' A##roach= To identi!$ !re5uent%$ occurringterm in each document 7orm a imi%arit$

meaure baed on the !re5uencie o! di!!erent

term Ce it to c%uter

' 3ain= In!ormation etriea% can uti%i.e the

c%uter to re%ate a ne& document or earch

term to c%utered document


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Illustrating Document Clustering

"%utering -oint= 9204 Artic%e o! Lo Ange%e Time

Simi%arit$ Meaure= 6o& man$ &ord are common in

thee document a!ter ome &ord !i%tering

Category Total

Articles

Correctly

Placed Financial 555 364

Foreign 341 260

National 273 36

Metro 943 746

Sports 738 573

Entertainment 354 278


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,ssociation 1ule Discoery:Defnition 3ien a et o! record each o! &hich contain ome

number o! item !rom a gien co%%ection

' -roduce de#endenc$ ru%e &hich &i%% #redict

occurrence o! an item baed on occurrence o! other

item

TID Items

1 Bread, Coke, Milk

2 Beer, Bread

3 Beer, Coke, Diaper, Milk

4 Beer, Bread, Diaper, Milk

5 Coke, Diaper, Milk

:ules Disco&ered;

2Mil(3 --4 2Co(e3 2Diaper5 Mil(3 --4 26eer3

:ules Disco&ered;

2Mil(3 --4 2Co(e3 2Diaper5 Mil(3 --4 26eer3


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,ssociation 1ule Discoery: ,pplication

Mar+eting and Sa%e -romotion= ' Let the ru%e dicoered be

{Bagels, … } --> {Potato Chips}

' -otato "hi# a cone5uent O "an be ued to

determine &hat hou%d be done to boot it a%e ' *age% in the antecedent O "an be ued to ee &hich

#roduct &ou%d be a!!ected i! the tore dicontinuee%%ing bage%

' *age% in antecedent and -otato chi# in cone5uent O "an be ued to ee &hat #roduct hou%d be o%d&ith *age% to #romote a%e o! -otato chi#


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,ssociation 1ule Discoery: ,pplication

.

Su#ermar+et he%! management

' 3oa%= To identi!$ item that are bought

together b$ u!!icient%$ man$ cutomer

' A##roach= -roce the #oint)o!)a%e data

co%%ected &ith barcode canner to !indde#endencie among item

' A c%aic ru%e ))

I! a cutomer bu$ dia#er and mi%+, then he i er$%i+e%$ to bu$ beer

So, donEt be ur#ried i! $ou !ind i)#ac+ tac+ed

net to dia#er


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Se7uential 8attern Discoery:Defnition

3ien i a et o! objects, &ith each obect aociated &ith it o&n timeline o e!ents, !ind ru%e that #redict trong e5uentia% de#endencie among di!!erent eent

u%e are !ormed b$ !irt dioering #attern @ent occurrence in the #attern are goerned b$ timing contraint

(A B) (C) (D E)

./ ms

./ x" 0n" ./ ws

(A B) (C) (D E)


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Se7uential 8attern Discoery:*+amples In te%ecommunication a%arm %og,

' InerterP-rob%em @ceiePLineP"urrent

ecti!ierPA%arm ))O 7irePA%arm

In #oint)o!)a%e tranaction e5uence,

' "om#uter *oo+tore=

IntroPToP>iua%P" "QQP-rimer ))O-er%P!orPdummie,Tc%PT+

' Ath%etic A##are% Store=

Shoe ac+et, ac+etba%% ))O S#ortPRac+et


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2;

1egression

-redict a a%ue o! a gien continuou a%ued ariab%e

baed on the a%ue o! other ariab%e, auming a

%inear or non%inear mode% o! de#endenc$

3reat%$ tudied in tatitic, neura% net&or+ !ie%d

@am#%e= ' -redicting a%e amount o! ne& #roduct baed on

adetiing e#enditure

' -redicting &ind e%ocitie a a !unction o!

tem#erature, humidit$, air #reure, etc ' Time erie #rediction o! toc+ mar+et indice


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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2<

Deiation9,nomaly Detection

Detect igni!icant deiation !rom norma% behaior

A##%ication=

' "redit "ard 7raud Detection

' Net&or+ Intruion

Detection

Typical network traffic at University level may reach over 100 million connections per day


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Challenges o& Data Mining

Sca%abi%it$ Dimeniona%it$

"om#%e and 6eterogeneou Data

Data Gua%it$ Data &nerhi# and Ditribution

-riac$ -reeration

Streaming Data

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