An 15 DM Caracterizacion

8/20/2019 An 15 DM Caracterizacion

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Data Mining:Characterization


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Concept Description:

Characterization and

Comparison What is concept description? Data generalization and summarization-based

characterization

Analytical characterization: Analysis of attribute

relevance

Mining class comparisons: Discriminating between

dierent classes Mining descriptive statistical measures in large

databases

ummary


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What is Concept

Description?

Descriptive vs! predictive data mining Descriptive mining: describes concepts or tas"-

relevant data sets in concise# summarative#

informative# discriminative forms $redictive mining: %ased on data and analysis#

constructs models for the database# and predictsthe trend and properties of un"nown data

&oncept description: &haracterization: provides a concise and succinctsummarization of the given collection of data

&omparison: provides descriptions comparing twoor more collections of data


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characterization


relevance



databases

ummary


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Data Generalization and

Summarization-based

Characterization Data generalization

A process which abstracts a large set of tas"-relevant data in a database from a low

conceptual levels to higher ones!12

3

4

5Conceptual levels


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Attribute-Oriented

Induction $roposed in '()( *+DD ,)( wor"shop .ot con/ned to categorical data nor particular

measures!

0ow it is done? &ollect the tas"-relevant data* initial relation using a

relational database 1uery

$erform generalization by attribute removal or

attribute generalization! Apply aggregation by merging identical# generalized

tuples and accumulating their respective counts!

2nteractive presentation with users!


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asic !rinciples o"

Attribute-Oriented

Induction Data focusing: tas"-relevant data# including dimensions# andthe result is the initial relation!

Attribute-removal: remove attribute A if there is a large set of

distinct values for A but *' there is no generalization

operator on A# or *3 A4s higher level concepts are e5pressedin terms of other attributes!

Attribute-generalization: 2f there is a large set of distinct

values for A# and there e5ists a set of generalization operators

on A# then select an operator and generalize A!

Attribute-threshold control: typical 3-)# speci/ed6default!

7eneralized relation threshold control: control the /nal

relation6rule size!


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#$ample

Describe general characteristics of graduatestudents in the %ig-8niversity database

use %ig98niversity9D%

mine characteristics as cience9tudents;

in relevance to name# gender# ma statement:Select name# gender# ma


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Class Characterization: An

#$ampleName Gender Major Birth-Place Birth_date Residence Phone # GPAJimoodman

M !" anco$%er&B!&

!anada

'-12-() 3511 Main "t*&

Richmond

)'(-45+' 3*)(

"cott

,achance

M !" Montreal& $e&

!anada

2'-(-(5 345 1st A%e*&

Richmond

253-+1.) 3*(.

,a$ra ,ee

/

0

/

Phsics

/

"eattle& A& "A

/25-'-(.

/

125 A$stin A%e*&

B$rna

/

42.-5232

/

3*'3

/

Remo%ed Retained "ci&n&B$s

!o$ntr Ae rane !it Remo%ed 6cl&G&**

Gender Major Birth_region Age_range Residence GPA Count

M Science Canada 20-25 Richond !er"-good #$

% Science %oreign 25-&0 Burna'" ()cellent 22

* * * * * * *

Birth_Region

Gender

Canada %oreign +otal

M #$ #, &0

% #0 22 &2

+otal 2$ &$ $2

Prime

Generali7ed

Relation

8nitial

Relation


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characterization


relevance



databases

ummary


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Attribute %ele&ance

Anal'sis Why? Which dimensions should be included?

0ow high level of generalization?

Automatic vs! interactive Beduce = attributesC easy to understand patterns

What? statistical method for preprocessing data

/lter out irrelevant or wea"ly relevant attributes

retain or ran" the relevant attributes

relevance related to dimensions and levels

analytical characterization# analytical comparison


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Attribute rele&ance

anal'sis (cont)d* 0ow? Data &ollection

Analytical 7eneralization

8se information gain analysis *e!g!# entropy or othermeasures to identify highly relevant dimensions andlevels!

Belevance Analysis ort and select the most relevant dimensions and levels!

Attribute-oriented 2nduction for class description n selected dimension6level

A$ operations *e!g! drilling# slicing on relevancerules


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%ele&ance Measures

>uantitative relevance measuredetermines the classifying power of

an attribute within a set of data! Methods

information gain *2DE

gain ratio *&F!G χ3 contingency table statistics

uncertainty coeHcient


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In"ormation-+heoretic

Approach Decision tree each internal node tests an attribute

each branch corresponds to attribute value

each leaf node assigns a classi/cation

2DE algorithm build decision tree based on training ob


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+op-Do,n Induction o"

Decision +reeAttri'utes ./utloo1 +eperature1 uidit"1 3ind4

/utloo

uidit" 3ind

sunn" rainovercast

"es

no "es

high noral

no

strong ea

"es

Pla"+ennis ."es1 no4


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#$ample: Anal'tical

Characterization Ias"

Mine general characteristics describing graduatestudents using analytical characterization

7iven attributes name, gender, major, birth_place,

birth_date, phone## and gpa

Gen(ai ) J concept hierarchies on ai Ui J attribute analytical thresholds for ai T i J attribute generalization thresholds for ai R J attribute relevance threshold


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#$ample: Anal'tical

Characterization (cont)d*

'! Data collection target class: graduate student

contrasting class: undergraduate student

3! Analytical generalization using 8i attribute removal

remove name and phone#

attribute generalization

generalize major # birth_place# birth_date and gpa accumulate counts

candidate relation: gender # major #birth_country # age_range and gpa


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#$ample: Anal'tical

characterization (*ender major irth_co$ntr ae_rane 9a co$ntM Science Canada 20-25 !er"_good #$

% Science %oreign 25-&0 ()cellent 22

M (ngineering %oreign 25-&0 ()cellent #6

% Science %oreign 25-&0 ()cellent 25

M Science Canada 20-25 ()cellent 2#

% (ngineering Canada 20-25 ()cellent #6

Candidate relation for Target class: Graduate students (

=120)

ender major irth_co$ntr ae_rane 9a co$nt

M Science %oreign 720 !er"_good #6

% Business Canada 720 %air 20M Business Canada 720 %air 22

% Science Canada 20-25 %air 2,

M (ngineering %oreign 20-25 !er"_good 22

% (ngineering Canada 720 ()cellent 2,

Candidate relation for Contrasting class: Undergraduate students ( =130)


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#$ample: Anal'tical

characterization (.*

E! Belevance analysis &alculate e5pected info re1uired to classify an

arbitrary tuple

&alculate entropy of each attribute: e!g! major

88660250

#&0

250

#&0

250

#20

250

#20#&0#20 222# .log log ) , I( ) s , I(s =−−==

%or major=”Science”9 S##6, S2#,2 :;s##1s2#


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#$ample: Anal'tical

Characterization (/* &alculate e5pected info re1uired to classify agiven sample if is partitioned according to theattribute

&alculate information gain for each attribute

2nformation gain for all attributes

B6B&0250

,2

250

62

250

#2$2&22#22### . ) s , s( I ) s , s( I ) s , s( I E(major) =++=

2##502# . E(major) ) s , I(s )Gain(major =−=

Gain;gender< 0=000&

Gain;'irth_countr"< 0=0,0

Gain;ajor< 0=2##5

Gain;gpa< 0=,,80

Gain;age_range< 0=58#


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#$ample: Anal'tical

characterization (0* F! 2nitial wor"ing relation *WK derivation B J K!'

remove irrelevant6wea"ly relevant attributes from candidaterelation JL drop gender # birth_country

remove contrasting class candidate relation

G! $erform attribute-oriented induction on WK using Ii

major ae_rane 9a co$nt

Science 20-25 !er"_good #$

Science 25-&0 ()cellent ,

Science 20-25 ()cellent 2#

(ngineering 20-25 ()cellent #6

(ngineering 25-&0 ()cellent #6

8nitial taret class :or;in relation .< Grad$ate st$dents


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characterization


relevance


dierent classes

Mining descriptive statistical measures in large

databases

ummary


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Mining Class Comparisons

&omparison: &omparing two or more classes! Method:

$artition the set of relevant data into the target classand the contrasting class*es

7eneralize both classes to the same high levelconcepts

&ompare tuples with the same high level descriptions $resent for every tuple its description and two

measures:

support - distribution within single class comparison - distribution between classes

0ighlight the tuples with strong discriminant features

Belevance Analysis: ind attributes *features which best distinguish

dierent classes!


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#$ample: Anal'tical

comparison

Ias" &ompare graduate and undergraduate

students using discriminant rule!

DM> 1uery

$se Big_niversit"_DBmine com9arison as @grad_vs_undergrad_students

in rele%ance to name, gender, major, birth_place, birth_date, residence, phone, gpa

=or @graduate_students

:here status in @graduate%ers$s @undergraduate_students

:here status in @undergraduateanal7e countE

=rom student


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#$ample: Anal'tical

comparison (* 7iven

attributes name, gender, major, birth_place,birth_date, residence, phone# and gpa

Gen(ai ) J concept hierarchies on attributes ai

Ui J attribute analytical thresholds for

attributes ai

T i J attribute generalization thresholds forattributes ai

R J attribute relevance threshold


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#$ample: Anal'tical

comparison (.*

'! Data collection target and contrasting classes

3! Attribute relevance analysis remove attributes name, gender, major, phone#

E! ynchronous generalization controlled by user-speci/ed dimension thresholds

prime target and contrasting class*esrelations6cuboids


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#$ample: Anal'tical

comparison (/*Birth_co$ntr Ae_rane G9a !o$nt>Canada 20-25 Good 5=5&E

Canada 25-&0 Good 2=&2E

Canada /ver_&0 !er"_good 5=6$E

* * * *

/ther /ver_&0 ()cellent ,=$6E

Prime enerali7ed relation =or the taret class< Grad$ate st$dents

Birth_co$ntr Ae_rane G9a !o$nt>

Canada #5-20 %air 5=5&E

Canada #5-20 Good ,=5&E

* * * *

Canada 25-&0 Good 5=02E

* * * *

/ther /ver_&0 ()cellent 0=$6E

Prime enerali7ed relation =or the contrastin class< nderrad$ate st$dents


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#$ample: Anal'tical

comparison (0*

F! Drill down# roll up and other A$ operationson target and contrasting classes to ad


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characterization

Analytical characterization: Analysis of attributerelevance


dierent classes


databases

ummary


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Mining Data Dispersion

Characteristics Motivation Io better understand the data: central tendency# variation

and spread

Data dispersion characteristics

median# ma5# min# 1uantiles# outliers# variance# etc!

.umerical dimensions correspond to sorted intervals

Data dispersion: analyzed with multiple granularities of

precision

%o5plot or 1uantile analysis on sorted intervals

Dispersion analysis on computed measures

olding measures into numerical dimensions

%o5plot or 1uantile analysis on the transformed cube


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Measuring the Central

+endenc' Mean

Weighted arithmetic mean

Median: A holistic measure

Middle value if odd number of values# or average ofthe middle two values otherwise

estimated by interpolation

Mode

Palue that occurs most fre1uently in the data

8nimodal# bimodal# trimodal

Qmpirical formula:

∑=

=n

i

i !

n !

#

#

∑

∑

=

==n

i

i

n

i

ii

"

!"

!

#

#

c #

l # n $median

median

<


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Measuring the Dispersion o"

Data >uartiles# outliers and bo5plots

>uartiles: >' *3Gth percentile# >E *RGth percentile

2nter-1uartile range: 2>B J >E S >'

ive number summary: min# >'# M# >E# ma5

%o5plot: ends of the bo5 are the 1uartiles# median is mar"ed#

whis"ers# and plot outlier individually

utlier: usually# a value higher6lower than '!G 5 2>B

Pariance and standard deviation

Pariance s2: *algebraic# scalable computation

tandard deviation s is the s1uare root of variance s2

∑ ∑∑= ==

−−

=−−

=n

i

n

i

ii

n

i

i !

n !

n ! !

n s

# #

22

#

22 G


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o$plot Anal'sis

ive-number summary of a distribution:

Minimum# >'# M# >E# Ma5imum

%o5plot

Data is represented with a bo5

Ihe ends of the bo5 are at the /rst and third1uartiles# i!e!# the height of the bo5 is 2B>

Ihe median is mar"ed by a line within thebo5

Whis"ers: two lines outside the bo5 e5tendto Minimum and Ma5imum


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A o$plot

A 'o)plot


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characterization

Analytical characterization: Analysis of attributerelevance


dierent classes


databases

ummary


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Summar'

&oncept description: characterization and

discrimination

A$-based vs! attribute-oriented induction

QHcient implementation of A2 Analytical characterization and comparison


databases

Discussion

2ncremental and parallel mining of description

Descriptive mining of comple5 types of data


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%e"erences

T! &ai# .! &ercone# and U! 0an! Attribute-oriented induction in relationaldatabases! 2n 7! $iatets"y-hapiro and W! U! rawley# editors# +nowledgeDiscovery in Databases# pages 3'E-33)! AAA26M2I $ress# '(('!

! &haudhuri and 8! Dayal! An overview of data warehousing and A$technology! A&M 27MD Becord# 3V:VG-RF# '((R

&! &arter and 0! 0amilton! QHcient attribute-oriented generalization for"nowledge discovery from large databases! 2QQQ Irans! +nowledge and DataQngineering# 'K:'(E-3K)# '(()!

W! &leveland! Pisualizing Data! 0obart $ress# ummit .U# '((E!

U! ! Devore! $robability and tatistics for Qngineering and the cience# Fth ed!Du5bury $ress# '((G!

I! 7! Dietterich and B! ! Michals"i! A comparative review of selected methodsfor learning from e5amples! 2n Michals"i et al!# editor# Machine earning: AnArti/cial 2ntelligence Approach# Pol! '# pages F'-)3! Morgan +aufmann# '()E!

U! 7ray# ! &haudhuri# A! %osworth# A! ayman# D! Beichart# M! Pen"atrao# !$ellow# and 0! $irahesh! Data cube: A relational aggregation operatorgeneralizing group-by# cross-tab and sub-totals! Data Mining and +nowledgeDiscovery# ':3(-GF# '((R!

U! 0an# T! &ai# and .! &ercone! Data-driven discovery of 1uantitative rules inrelational databases! 2QQQ Irans! +nowledge and Data Qngineering# G:3(-FK#'((E!


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%e"erences (cont1*

U! 0an and T! u! Q5ploration of the power of attribute-oriented induction in datamining! 2n 8!M! ayyad# 7! $iatets"y-hapiro# $! myth# and B! 8thurusamy#editors# Advances in +nowledge Discovery and Data Mining# pages E((-F3'!AAA26M2I $ress# '((V!

B! A! Uohnson and D! A! Wichern! Applied Multivariate tatistical Analysis# Erded! $rentice 0all# '((3!

Q! +norr and B! .g! Algorithms for mining distance-based outliers in large

datasets! PD%()# .ew Tor"# .T# Aug! '(()! 0! iu and 0! Motoda! eature election for +nowledge Discovery and Data

Mining! +luwer Academic $ublishers# '(()!

B! ! Michals"i! A theory and methodology of inductive learning! 2n Michals"i etal!# editor# Machine earning: An Arti/cial 2ntelligence Approach# Pol! '# Morgan+aufmann# '()E!

I! M! Mitchell! Persion spaces: A candidate elimination approach to rule

learning! 2U&A2(R# &ambridge# MA! I! M! Mitchell! 7eneralization as search! Arti/cial 2ntelligence# '):3KE-33V#

'()3!

I! M! Mitchell! Machine earning! Mc7raw 0ill# '((R!

U! B! >uinlan! 2nduction of decision trees! Machine earning# ':)'-'KV# '()V!

D! ubramanian and U! eigenbaum! actorization in e5periment generation!AAA2)V# $hiladelphia# $A# Aug! '()V!

An 15 DM Caracterizacion

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