Crisp 12 Pre

8/20/2019 Crisp 12 Pre

http://slidepdf.com/reader/full/crisp-12-pre 1/81

ECML/PKDD-2003Knowledge DiscoveryStandards

Tutorial resented !y"Sara! #nand $%niversity o& %lster'(

Mar)o *ro!elni) $+nstitute ,oe& Ste&an' and Dietric. ettsc.erec) $T.e o!ert *ordon

%niversity' Tuesday( 231 Sete!er 2003

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ECML/PKDD 2003 : KD-Standards Tutorial S. Anand, M. Groblni!, D. "tts#$r#!

Tutorial !4ectives

verview o& e5isting KD-standards Motivation &or using KD-standards

6ow do t.ese standards relate toeac. ot.er7

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*lo!al view" C+SP-DM

Model generation" ,DMS8L/MM(LED9 DM

Data access"S8L

inter&aces

Model reresentation" PMML

T.e Knowledge Discovery

Process Data access"S8L

inter&aces

Model reresentation" PMML

Model generation" ,DMS8L/MM(LED9 DM

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Tutorial utline

+ntroduction C+SP-DM S8L inter&aces &or Data Mining 9rea) ,ava Data Mining #P+

Predictive Model Mar)-u Language E5ales

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C+SP-DM" # Standard

Process Model &or DataMining.tt"//www1cris-d1org/

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.at is C+SP-DM7

Cross-+ndustry Standard Process &or Data Mining #i"

To develo an industry( tool and alication neutralrocess &or conducting Knowledge Discovery

De:ne tas)s( oututs &ro t.ese tas)s( terinologyand ining ro!le tye c.aracteriation

;ounding Consortiu Me!ers" DailerC.rysler(SPSS and <C

C+SP-DM Secial +nterest *rou = 200 e!ers Manageent Consultants Data are.ousing and Data Mining Practitioners

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http://slidepdf.com/reader/full/crisp-12-pre 7/81ECML/PKDD 2003 : KD-Standards Tutorial S. Anand, M. Groblni!, D. "tts#$r#!

;our Levels o& #!straction P.ases

E5ale" Data Prearation

*eneric Tas)s # sta!le( general and colete set o& tas)s E5ale" Data Cleaning

Secialied Tas) 6ow is t.e generic tas) carried out E5ale" Missing >alue 6andling

Process +nstance E5ale" T.e ean value &or nueric attri!utes and

t.e ost &re?uent &or categorical attri!utes wasused

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P.ases o& C+SP-DM

<ot linear( reeatedly !ac)trac)ing

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9usiness %nderstanding

P.ase %nderstand t.e !usiness o!4ectives .at is t.e status ?uo7

%nderstand !usiness rocesses #ssociated costs/ain

De:ne t.e success criteria Develo a glossary o& ters" sea) t.e language Cost/9ene:t #nalysis

Current Systes #ssessent +denti&y t.e )ey actors

Miniu" T.e Sonsor and t.e Key %ser .at &ors s.ould t.e outut ta)e7 +ntegration o& outut wit. e5isting tec.nology landscae %nderstand ar)et nors and standards

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9usiness %nderstanding

P.ase Tas) Decoosition 9rea) down t.e o!4ective into su!-tas)s Ma su!-tas)s to data ining ro!le de:nitions

+denti&y Constraints esources Law e1g1 Data Protection

9uild a ro4ect lan List assutions and ris)

$tec.nical/:nancial/!usiness/ organisational'&actors

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Data %nderstanding P.ase Collect Data

.at are t.e data sources7 +nternal and E5ternal Sources $e1g1 #5io(

E5erian' Docuent reasons &or inclusion/e5clusions Deend on a doain e5ert #ccessi!ility issues

Legal and tec.nical

#re t.ere issues regarding data distri!utionacross di@erent data!ases/legacy systes .ere are t.e disconnects7

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Data %nderstanding P.ase

++ Data Descrition Docuent data ?uality issues

re?uireents &or data rearation

Coute !asic statistics

Data E5loration Sile univariate data lots/distri!utions +nvestigate attri!ute interactions Data 8uality +ssues

Missing >alues %nderstand its source" Missing vs <ull values

Strange Distri!utions

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Data Prearation P.ase

+ntegrate Data ,oining ultile data ta!les Suarisation/aggregation o& data

Select Data #ttri!ute su!set selection

ationale &or +nclusion/E5clusion Data saling

Training/>alidation and Test sets

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Data Prearation P.ase ++ Data Trans&oration

%sing &unctions suc. as log ;actor/Princial Coonents analysis <oraliation/Discretisation/9inarisation

Clean Data 6andling issing values/utliers

Data Construction Derived #ttri!utes

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T.e Modelling P.ase Select o& t.e aroriate odelling

tec.ni?ue Data re-rocessing ilications

#ttri!ute indeendence

Data tyes/<oralisation/Distri!utions Deendent on

Data ining ro!le tye utut re?uireents

Develo a testing regie Saling

>eri&y sales .ave siilar c.aracteristics and arereresentative o& t.e oulation

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T.e Modelling P.ase 9uild Model

C.oose initial araeter settings Study odel !e.aviour

Sensitivity analysis

#ssess t.e odel 9eware o& over-:tting +nvestigate t.e error distri!ution

+denti&y segents o& t.e state sace w.ere t.e odel is

less e@ective +teratively ad4ust araeter settings

Docuent reasons o& t.ese c.anges

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T.e Evaluation P.ase >alidate Model

6uan evaluation o& results !y doaine5erts

Evaluate use&ulness o& results &ro !usiness

ersective De:ne control grous Calculate li&t curves E5ected eturn on +nvestent

eview Process Deterine ne5t stes

Potential &or deloyent Deloyent arc.itecture Metrics &or success o& deloyent

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T.e Deloyent P.ase Knowledge Deloyent is seci:c to

o!4ectives Knowledge Presentation Deloyent wit.in Scoring Engines and

+ntegration wit. t.e current +T in&rastructure #utoated re-rocessing o& live data &eeds AML inter&aces to 3rd arty tools

*eneration o& a reort nline/Bine

Monitoring and evaluation o& e@ectiveness Process deloyent/roduction Produce :nal ro4ect reort

Docuent everyt.ing along t.e way

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Microso&t LE D9 &or DM

E5tension o& Microso&t

#nalysis Services &or DataMining

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.at is LE D9 &or Data-

Mining7 LE D9 &or DM is Microso&ts E5tension o&#nalysis Server roduct &or covering DM&unctionality +t is closely connected to MS L#P Server or)s wit.in S8L Server data!ase suite

+t de:nes DM at several levels" E5tensions o& S8L language &or descri!ing DM

tas)s

#P+ in t.e &or o& CM inter&ace &or" $F' Prograing DM clients wit.in alications $2' Prograing DM roviders $server side coonents'

&or including new DM algorit.s %ses PMML &or odel descrition

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#rc.itecture o& a solution using

LE D9 &or DM tec.nologyEnd-%ser #lication

Data!ase SystesMS S8L Server(MS L#P Server

racle( D92( G

MS E5cel /MS Site Server /

MS Coerce Server

MS #nalysis Server

Decision TreesCoonent

ClusteringCoonent

LE D9 &or DM

LE D9 &or DMLE D9 &or DM

LE D9 &or DM

LE D9

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.at are )ey DM tas)s7 Key DM tas)s covered !y LD D9 &or

DM are" Predictive Modeling $Classi:cation' Segentation $Clustering' #ssociation $Data Suariation' Se?uence and Deviation #nalysis

Deendency Modeling

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De:ning a doain H

Creating Mining Model !4ectUsing an OLE DB command object, the clientexecutes a CREATE statement that is similar to aCREATE TABLE statement:

CE#TE M+<+<* MDEL I#ge PredictionJ$ICustoer +DJ L<* KE(I*enderJ TEAT D+SCETE(I#geJ D%9LE D+SCET+ED$' PED+CT(IProduct Purc.asesJ T#9LE $IProduct <aeJ TEAT KE(

I8uantityJ D%9LE <M#L C<T+<%%S(IProduct TyeJ TEAT D+SCETE EL#TED T IProduct <aeJ'

'%S+<* IDecision TreesJ

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+nserting Training Data intoModel

n a manner similar to !o!ulating an ordinar" table,the client uses a #orm o# the $%ERT $TO

statement&$ote the use o# the %'A(E statement to create thenested table&

+<SET +<T I#ge PredictionJ$ICustoer +DJ( I*enderJ( I#geJ(IProduct Purc.asesJ$SK+P( IProduct <aeJ( I8uantityJ( IProduct TyeJ'

'S6#PE

SELECT ICustoer +DJ( I*enderJ( I#geJ ;M Custoers DE 9

ICustoer +DJN#PPE<D $

SELECT ICust+DJ( IProduct <aeJ( I8uantityJ( IProduct TyeJ ;M SalesDE 9 ICust+DJNEL#TE ICustoer +DJ To ICust+DJ'

#S IProduct Purc.asesJ

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%sing Models to a)e

Predictions(redictions are made )ith a %ELECT statement that joinsthe

model*s set o# all !ossible cases )ith another set o# actualcases&

SELECT t1ICustoer +DJ( I#ge PredictionJ1I#geJ;M I#ge PredictionJPED+CT+< ,+< $

S6#PE SELECT ICustoer +DJ( I*enderJ( ;M Custoers DE 9 ICustoer +DJN

#PPE<D $SELECT ICust+DJ( IProduct <aeJ( I8uantityJ ;M Sales DE 9 ICust+DJNEL#TE ICustoer +DJ To ICust+DJ

'#S IProduct Purc.asesJ

' as t< I#ge PredictionJ 1*ender O t1*ender and

I#ge PredictionJ 1IProduct Purc.asesJ1IProduct <aeJ O t1IProduct Purc.asesJ1IProduct <aeJ andI#ge PredictionJ 1IProduct Purc.asesJ1I8uantityJ O t1IProduct Purc.asesJ1I8uantityJ

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#ssociation ules The #ollo)ing statement creates a data mining

model to +nd out those !roducts )hich sell togetherbased on an association algorithm& The model isinterested onl" in rules )ith at least +e items:

Create Mining Model My#ssociationModel $

Transactionid long )ey( IProduct urc.asesJ ta!le redict $ IProduct <aeJ te5t )ey ' '%sing IMy #ssociation #lgorit.J $Miniusie O Q'

Training an association model is exactl" the same as

training a tree model or a clustering model& To get all the association rules discoered b" the

algorithm, run the #ollo)ing statement:Select R &ro My#ssociationModel1content

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egression #nalysis B" using a regression algorithm, the #ollo)ing

mining model !redicts loan ris- leel based onage, income, homeo)ner, and marital status:Create Mining Model MyegressionModel $

Custoerid long )ey(#ge long continuous(

6oeowner !oolean discrete(

Maritalstatus 9oolean discrete(

Loanris)LE>ELcontinuous redict

'%sing IMy egression #lgorit.J

The #ollo)ing statement returns all thecoe.cients o# the regression:Select R &ro MyegressionModel1content

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>isual 9asic e5ale using t.e

LE D9 &or DM Clusteringcoonent$F' Di ClusterConnection #s <ew #DD91Connection$2' ClusterConnection1Provider O MSDMine$3' DMM<ae O ICollPlanDMMJ$' Data;ile<ae O 1UCollegePlan1d!

$Q' ClusterConnection1ConnectionString O locationOlocal.ostVW initial catalogOI;oodMart 2000JV$X' ClusterConnection1en

$Y' ClusterConnection1E5ecute CE#TE M+<+<* MDEL IClusterModelJW $IStudent +dJ L<* KE( ICollege PlansJ TEAT D+SCETEPED+CT(

W I*enderJ TEAT D+SCETE PED+CT( I+?J L<* C<T+<%%SPED+CT(W IParent EncourageentJ TEAT D+SCETE PED+CT( IParent+ncoeJL<* C<T+<%%S PED+CT'W %S+<* Microso&tClustering

$Z' G

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AML# - AML &or #nalysis

.tt"//5la1org/

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.at is AML &or #nalysis7

/0L #or Anal"sis is a set o& AML Message+nter&aces t.at use t.e industry standard S#P tode:ne t.e data access interaction !etween a

client alication and an analytical data rovider$L#P and Data Mining' wor)ing over t.e+nternet1

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.at are t.e !ene:ts o&

AML#7 Customers will gain t.e a!ility to rotect server

and tools investents and ensure t.at newanalytical deloyents will interoerate and wor)cooeratively1

Deelo!ers will gain t.e a!ility to leveragee5isting develoer s)ills and to use oen accessAML-!ased e! services( eliinating t.e need torogra to ultile #P+s and ?uery languages1

nde!endent so#t)are endors will !e a!le toreduce cole5ity and costs &or develoent andaintenance !y writing to a single access inter&ace1

http://xmla.org/

http://xmla.org/

http://xmla.org/

http://xmla.org/

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6istory o& AML#

2000 2001 2002 2003

Hyperion & MicrosoftAnnounce Co-Sponsorshipof XMLA Specification

SAS Joins Council

First XMLA CouncilMeetin !creation of S"# tea$s%

Microsoft eleases S'(

)ersion 1*0 elease+

)ersion 1*1 elease+

)ersion 1*2 !,'%

Apr .o/ MayAprApr Sep

"nterperate orshop "

"nterperate orshop ""

Mar

Secon+ XMLA CouncilMeetin

1st Public XMLAInterOperabilityDemonstration(TDWI)

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E5ale o& AML# S#P

e?uest T.e &ollowing is an e5ale o& an Execute et.od callwit. [Stateent\ set to an L#P MDA SELECT stateent"

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E5ale o& AML# S#P

esonse T.is is t.e a!!reviated resonse &or t.ereceding et.od call"

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.at Provider >endors SuortAML#7

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.at Consuer W Consulting>endors #re/ill Suort AML#7

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9E#K

http://www.xmla.org/

http://www.xmla.org/

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,DM" T.e ,ava #P+ &or DataMining

http://www.informatica.com/

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!4ective To develo a ,ava #P+ t.at suorts

9uilding o& odels Scoring o& data using odels Creation( storage( access and aintenance o& data

and etadata suorting data ining results To provide for data mining systems what JDBCTM did for

relational databases Implementers of data mining applications can expose a

single, standard AI !nderstood by a wide variety of client

applications and components Data Mining clients can be coded against a single AI that

is independent of the !nderlying data mining system "vendor

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#roac. and

Develoent Leverages ot.er related standards

PMML $DM*' CM $M*' S8L/MM $+S' ,CA $,S-FX'

Pu!lic Dra&t eleased in ,uly( 2002

Currently wor) is continuing on t.e:nal dra&t

,M+ $,S-0' ,L#P $,S-X]'

C+SP-DM LED9 DM

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elated Standards

DMG

PMML

%&rsntation o' data

(inin) (odls 'or intr-*ndor +#$an)

DTD/ML

MG

C"M

DM

b#t (odl

'or r&rsntin)

data (inin) (tadata:(odls, (odl rsults

1ML/DTD/ML

SL/MM

Pt. DM

SL ob#ts 'or d'inin),#ratin), and a&&l4in)

data (inin) (odls, and

obtainin) t$ir rsults

SL

LE D5

'or DM

SL-li! intr'a#'or data (inin)

o&rations

LE D5/SL

6S%-073

6DM

6a*a AP8 'or d'inin),#ratin), a&&l4in), and

obtainin) t$ir rsults o'

data (inin) (odls

6a*a

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T.e E5ert *rou Mar) 6ornic)( racle

$Lead' 9E# Systes Couter #ssociates

Cororate+ntellect CalTec. ;air +ssac 6yerion +9M KAE<

8uadstone S#P S#S SPSS Strategic

#nalytics Sun Microsystes %niversity o&

%lster

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%se Case # rograer is tas)ed wit. develoent o& a target

ar)eting tools t.at allows t.e user to C.oose a target caaign E-ail a rando sale o& t.e custoers 9uild a odel !ased on t.e resonses

#ly t.e odel to irove t.e targeting o& t.e caaign %sing ,DM $&or t.e 3rd and t. tas)s' t.e rograer

De:nes t.e target data &or t.e odelling using t.e P.ysical andLogical Data Classes

%ses t.e Classi:cation ;unction Settings class to set de&aultaraeters &or t.e learning tas)

Creates a !uild tas) t.at generates and ersists t.e odel Creates an aly tas) t.at alies t.e odel to select t.e caaign

targets Miniises ris) associated wit. a c.ange in t.e data ining vendor !y

using t.e standard ,DM inter&ace

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6ow will it wor)7

,DM de:nes a set o&inter&aces &or De:ning t.e data to !e

used in t.e ining P.ysical/Logical Data

De:ning t.e dataining araeters ;unction settings

Suort &or <ovice%sers

#lgorit. settings E5ert %ser #lgorit. seci:c

settings

Per&oring Tas)s E5ecuting a data ining

algorit. +orting/E5orting to

PMML Testing t.e )nowledge #lying t.e )nowledge on

new data 9atc. and eal-tie

Scoring

Coute Statistics

+nterrogating t.e resulting)nowledge

Persistence o& all MetaData/Data

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Tyical #rc.itecture

, D M

Cororateare.ouse

MetaData

eository

ProrietaryDataMiningEngine F

MetaDataeository

ProrietaryData MiningEngine 2

1

1

%ses ;actory Classes6ence( Service ProviderClasses need not!e ade u!lic

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Con&orance ules &or ServiceProviders

a la carte aroac. to &unctions and algorit.ssuorted vendors ileent &unctions and algorit.s t.at t.eir

roducts suort

#t least one &unction ust !e suorted #ll core ac)ages ust !e suorted #ll et.ods wit.in a ileented class ust !e

ileented seantics seci:ed &or eac. et.od ust !e

ileented to ensure coon interretation o& a givenresult

Must suort ,2EE and/or ,2SE E5tension ay !e done t.roug. su!classing

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Data Mining ;unctions

Suorted Classi:cation egression #ttri!ute +ortance Clustering #ssociation ules

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#lgorit.s Suorted <a^ve 9ayes Decision Trees ;eed ;orward <eural <etwor)s Suort >ector Mac.ines K-Means

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Code E5ale $F'// Gt a #onn#tion

9 Conn#tionS&# #onnS&# a*a+.data(inin).rsour#.Conn#tionS&# d(C;a#tor4.)tConn#tionS&#<2 #onnS&#.st=a( >usr9? <

3 #onnS&#.stPass@ord >&s@d? <

#onnS&#.st1%8 >(4DME? <

B a*a+.data(inin).rsour#.Conn#tion d(Conn d(C;a#tor4.)tConn#tion#onnS&# <

// Crat and &o&ulat t$ P$4si#al Data ob#t D'in t$ Data to b usd

P$4si#alDataSt;a#tor4 &ds;a#tor4 P$4si#alDataSt;a#tor4 d(Conn.)t;a#tor4 > a*a+.data(inin).data.P$4si#alDataSt? <

7 P$4si#alDataSt &d &ds;a#tor4.#rat >(ini*an.data? <

&d.i(&ortMtaData<

d(Conn.sa*b#t >(4PD?, &d <

// Crat Lo)i#alData ob#t

90 Lo)i#alData;a#tor4 ld;a#tor4 Lo)i#alData;a#tor4 d(Conn.)t;a#tor4>a*a+.data(inin).data.Lo)i#alData? <

99 Lo)i#alData ld ld;a#tor4.#rat &d <

// S&#i'4 $o@ attributs s$ould b usd

92 Lo)i#alAttribut in#o( ld.)tAttribut >in#o(? <

93 in#o(.stAttributT4& AttributT4&.nu(ri#al <

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Code E5ale $2'// Crat t$ ;un#tionSttin)s 'or Classi'i#ation

9 Classi'i#ationSttin)s;a#tor4 #'s;a#tor4 Classi'i#ationSttin)s;a#tor4 d(Conn.)t;a#tor4

>a*a+.data(inin).su&r*isd.#lassi'i#ation.Classi'i#ationSttin)s? <

9B Classi'i#ationSttin)s sttin)s #'s;a#tor4.#rat<

9 sttin)s.stTar)tAttribut=a( >bu4Mini*an? <

97 sttin)s.stCostMatri+ #osts < // &rd'ind #ost (atri+

// Crat t$ Al)orit$(Sttin)s and add it to t$ ;un#tionSttin)s

9 =ai*5a4sSttin)s;a#tor4 nb;a#tor4 =ai*5a4sSttin)s;a#tor4 d(-Conn.)t;a#tor4

>a*a+.data(inin).al)orit$(.nai*ba4s.=ai*5a4s-Sttin)s? <

9 =ai*5a4sSttin)s nbSttin)s nb;a#tor4.#rat<

20 nbSttin)s.stSin)ltonT$rs$old .09L <

29 nbSttin)s.stPair@isT$rs$old .09L <

// Asso#iat LD and AS @it$ t$ ;un#tionSttin)s

22 sttin)s.stAl)orit$(Sttin)s nbSttin)s <

23 sttin)s.stLo)i#alData ld <

2 d(Conn.sa*b#t >(4;S?, sttin)s <

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Code E5ale $3'// Crat t$ build tas!

2 5uildTas!;a#tor4 bt;a#tor4

5uildTas!;a#tor4 d(Conn.)t;a#tor4>a*a+.data(inin).tas!.5uildTas!? <

27 5uildTas! buildTas! bt;a#tor4.#rat >(4PD?, >(4;S?, >(4Modl? <

2 Fri'i#ation%&ort r&ort buildTas!.*ri'4<

2 i' r&ort null H // it$r rror or @arnin)

30 %&ortT4& r&ortT4& r&ort.)t%&ortT4& < // #$#! i' itIs ust a @arnin) or an rror

32 J ls H

33 d(Conn.sa*b#t >(45uildTas!?, buildTas! <

// E+#ut t$ tas! and blo#! until 'inis$d

3 E+#utionandl $andl d(Conn.+#ut >(45uildTas!? <

3B $andl.@ait;orCo(&ltion null < // @ait @it$out ti(out until don

// A##ss t$ (odl

3 Classi'i#ationModl (odl

Classi'i#ationModl d(Conn.)tb#t >(4Modl?, =a(db#t.(odl <

37 J

// Clos t$ #onn#tion

3 d(Conn.#los<

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PMML" T.e PredictiveModel Mar)u Language

.tt"//www1dg1org

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Predictive Model Mar)-u Language$PMML'

+ndustry led standard &or reresentingt.e outut o& data ining

Suorted !y ;ull Me!ers" +9M( racle( Magni&y( SPSS(

S#S( StatSo&t( Microso&t( Cororate+ntellect(KAE<( Sal&ord Systes

<uerous #ssociated Me!ers

!4ective de:ne and s.are redictive odels using an

oen standard

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ationale

Cole5 osaic o& so&tware alications Knowledge generators

Data Mining >endors Di@erent data ining algorit.s .ave di@erent

languages &or e5ressing t.e )nowledge discovered >endor deendent reresentations &or )nowledge e1g1

C/C__ routines

Knowledge consuers eal-tie Scoring / Personalisation engines

Mar)eting Tools >isualisation Tools

<eed &or a vendor indeendentreresentation o& data ining outut

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PMML

9ene:ts rorietary issues and incoati!ilities no

longer a !arrier to t.e e5c.ange o& odels!etween alications

!ased on AML develo odels using any generator

vendor( deloy t.e odels using anyconsuer vendor alication

Develoent Current elease 21F Suorted !y ost current releases o&

e!er vendors alications

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PMML Docuent 9asic AML structure DCTPE declaration

not re?uired # PMML docuent ust

!e a valid AML docuent o!ey PMML con&orance

rules

oot eleent [PMML\ X c.ild eleents

2 re?uired 6eader Data Dictionary

otional

+(l *rsionN9.0N O

[`DCTPE PMML P%9L+C PMML210

.tt"//www1dg1org/v2-0/lv201dtd\

PMML *rsionN2.0N O

adr /O

Minin)5uildTas! /O

DataDi#tionar4 /O

Trans'or(ationDi#tionar4 /O

SQun#Minin)Modl /O

E+tnsion /O

/PMMLO

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6eader #ttri!utes

coyrig.t descrition

Eleents #lication $t.at generated t.e PMML'

<ae" Cari >ersion" 210

#nnotation ;ree te5t

TieSta Date/Tie o& odel creation

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6eader $2'

+(l *rsionN9.0N O

PMML *rsionN9.0N O

adr #o&4ri)$t>Cor&orat8ntll#tN ds#ri&tion>%sults o' CAP%8N O

/adrO

. . .

. . .

/PMMLO

A&&li#ation na(>C%ALN *rsionN3.0N OAnnotationOT$is is a PMML do#u(nt @it$ rsults 'ro( t$

CAP%8 run on #o((odit4 (ar!t data./AnnotationO

Ti(sta(&O2003-03-02 9:30:00 GMT R00:00/Ti(sta(&O

http://www.dmg.org/v2-0/pmml_v2_0.html



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Mining 9uild Tas) May contain any AML value descri!ing t.e

con:guration o& t.e training run t.atroduced t.e odel

+n&oration rovided in t.is eleent isessentially eta-data not used seci:cally in t.e deloyent o& t.e

odel !y t.e PMML consuer

Seci:c content structure not de:ned inPMML

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Data Dictionary #ttri!utes

<u!er o& ;ields aids consistency c.ec)s

Eleents Data;ield

#ttri!utes <ae dislay<ae tye

categorical/ordinal/continuous De:nes legal oerations on t.e :eld values

Ta5onoy #ame of taxonomy that defines a hierarchy on the val!es

isCyclic

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Data Dictionary $2'

Eleents >alue

De:nes doain &or ordinal and categorical attri!utes value dislay>alue roerty" valid/ invalid/ issing

+nterval De:nes t.e range o& valid values &or continuous

:elds closure" oenClosed( closeden( oenen(

closedClosed le&tMargin

rig.tMargin Ta5onoy

De:ne .ierarc.ies on seci:c :elds wit.in t.e datadictionary

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Data Dictionary $3'

#ttri!utes nae" associates t.e ta5onoy wit. t.e aroriate :eld

wit.in t.e data dictionary $see Data;ield attri!ute ta5onoy'

Eleents C.ildParent

#ttri!utes c.ild;ield" nae o& :eld wit.in t.e ta!le $see Eleents

!elow' t.at reresents t.e c.ild value arent;ield" nae o& :eld wit.in t.e ta!le $see Eleents

!elow' t.at reresents t.e arent value arentLevel;ield" nae o& :eld wit.in t.e ta!le $see

Eleents !elow' t.at reresents t.e level in t.e.ierarc.y isecursive" es/<o" i& t.e w.ole .ierarc.y is de:ned in

t.e sae ta!le or an individual ta!le er level Eleents

+nline Ta!le/Ta!le Locator

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DataDictionary colete

+(l *rsionN9.0N O

PMML *rsionN9.0N O

adr /O

. . .

/PMMLO

DataDi#tionar4 nu(';ilds N3N O

/DataDi#tionar4 O

Data;ild na( NT4&N o&t4&N#at)ori#alNO

Falu *alu N51 N/O Falu *alu NN/O

Falu *alu NCN/O

/Data;ildO

Data;ild na( NA)N o&t4& N#ontinuousNO

8ntr*al #losur N#losdClosdN l'tMar)in N0N ri)$tMar)in N9B0N/O

/Data;ildO

Data;ild na( NPostCodN o&t4&N#at)ori#alN ta+ono(4 NLo#ationN /O

Ta+ono(4 naeOLocation\ .

/Ta+ono(4O

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Ta5onoy E5ale[Ta5onoy naeOLocation\[C.ildParent c.ildColunOPost Code arentColunODistrict\

[Ta!leLocator 5-d!naeOyD9 5-ta!le<aeOPostCodeDistrict /\[/C.ildParent\[C.ildParent c.ildColunOe!er arentColunOgrouisecursiveOyes\ [+nlineTa!le\

[E5tension e5tenderOMySyste\ [row e!erO] grouOCentralLondon/\

[row e!erO<] grouO<ort.London/\[row e!erO<2 grouO<ort.London/\[row e!erOF grouOCentralLondon/\[row e!erOCentralLondon grouOLondon/\

[row e!erO<ort.London grouOLondon/\[row e!erOEastLondon grouOLondon/\[row e!erOLondon grouOEngland/\GGGG1

[/E5tension\[/+nlineTa!le\[/C.ildParent\ [/Ta5onoy\

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Trans&oration Dictionary De:nes aing o& source data values

to values ore suited &or use !y t.eining algorit.

PMML suorts Normalization$ map val!es to n!mbers, the inp!t

can be contin!o!s or discrete% Discretization$ map contin!o!s val!es to discrete

val!es%

Value mapping$ map discrete val!es to discreteval!es%

Aggregation$ s!mmari&e or collect gro!ps ofval!es, e%g% comp!te average

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Trans&oration Dictionary

$2' Tran&orationDictionary Derived;ield Eleents

#ttri!utes nae

dislay<ae Eleents

E5ression $one o& t.e &ollowing' <orContinuous <orDiscrete Discretie Ma>alues #ggregates

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$3'[Derived;ield naeOnoral#ge\

[<orContinuous :eldOage\[Linear<or origOQ norO0/\

[Linear<or origOZ2 norO01Q/\ [Linear<or origOF0Q norOF/\

[/<orContinuous\[/Derived;ield\[Derived;ield naeOale\

[<orDiscrete :eldOarital statusvalueO/\[/Derived;ield\[Derived;ield naeO&eale\

[<orDiscrete :eldOarital statusvalueO&/\[/Derived;ield\

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$'[Derived;ield naeO!innedPro:t\ [Discretie :eldOPro:t\

[Discretie9in !in>alueOnegative\[+nterval closureOoenen rig.tMarginO0 /\

[/Discretie9in\[Discretie9in !in>alueOositive\

[+nterval closureOcloseden le&tMarginO0 /\[/Discretie9in\

[/Discretie\[/Derived;ield\[Derived;ield naeO.ouseTye\ [Ma>alues oututColunOlong;or\

[;ieldColunPair :eldOTye colunOs.ort;or/\[+nlineTa!le\[E5tension\

[row\[s.ort;or\9%[/s.ort;or\[long;or\!unglow[/long;or\ [/row\[row\[s.ort;or\6[/s.ort;or\[long;or\.ouse[/long;or\

[/row\[row\[s.ort;or\C[/s.ort;or\[long;or\cottage[/long;or\

[/row\ [/E5tension\[/+nlineTa!le\

[/Ma>alues\[/Derived;ield\

[Derived;ield naeOites9oug.t\ [#ggregate :eldOite &unctionOultiset grou;ieldOtransaction/\

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T.e PMML Docuent

Data Dictionary


Mining Sc.ea

ModelF

G

Model2 Model)

Data

ModelStatistics

Mining Sc.ea

ModelStatistics

Mining Sc.ea

ModelStatistics

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Mining Sc.ea Eleents

Mining;ield #ttri!utes

<ae

usageTye" active/ redicted/ suleentary utliers" as+s/ asMissing>alue/ asE5tree>alues low>alue .ig.>alue issing>alueelaceent issing>alueTreatent" as+s/ asMean/ asMode/

asMedian/ as>alue

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+(l *rsionN9.0N OPMML *rsionN9.0N O

adr /O

DataDi#tionar4 /O

/PMMLO

MiningSc.ea

SQun#Modl &unction<aeOse?uences algorit.<aeOCari2iniuSuortO21FY iniuCon:denceO0100

nu!er&+tesOQ nu!er&SetsOQ nu!er&Se?uencesOFFnu!er&ulesO3\[E5tension naeOorder!y valueOnone/\

/SQun#Modl O

Minin)S#$(a O

/Minin)S#$(a O

Minin);ild na( NPri#N usa)T4&N&rdi#tdN /O

Minin);ild na( Nlo#ationN usa)T4&Na#ti*N /O

Minin);ild na( Nbdroo(sN usa)T4&Na#ti*N /O

Minin);ild na( N$ousT4&N usa)T4&Na#ti*N /O

Minin);ild na(NAraN usa)T4& Nsu&&l(ntar4N /O

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Model Statistics Eleents

%nivariateStatistics #ttri!utes

;ield

Eleents Discrete Statistics Continuous Statistics Counts" >alid( +nvalid and Missing counts <ueric+n&o" in/ a5/ ean/ standard

deviation/ edian/ inter8uartileDistance

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Suorted Data Mining

Models Tree Model <eural <etwor)s Clustering Model

egression Model *eneral egression Model <a^ve 9ayes Model

#ssociation ules Se?uence ule Model

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Se?uence Model

eresents t.e oututo& Se?uence uleMining

#ttri!utes odel<ae &unction<ae algorit.<ae nu!er&Transactions

iniuSuort iniuCon:dence lengt.Liit G11

Eleents Se?uence ule

Eleents #ntecedent Se?uence

se?uencee&erenc

e Conse?uent Se?uence Deliiter

Se?uence Eleents

Sete&erence Deliiter

Set Predicate #rray

[Se?uenceModel &unction<aeOse?uences nu!er&TransactionsOF00iniuSuortO0120 iniuCon:denceO012Q nu!er&+tesOXnu!er&Sets Q nu!er&Se?uences 3 nu!er&ules F\ [MiningSc.ea\

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nu!er&SetsOQ nu!er&Se?uencesO3 nu!er&ulesOF\ [MiningSc.ea\GGG [/MiningSc.ea\

[SetPredicate idOs00F :eldOtransaction oeratorOsuerset&\[#rray nOF tyeOstring\ inde51.tl [/#rray\ [/SetPredicate\

[SetPredicate idOs002 :eldOtransaction oeratorOsuerset&\[#rray nO2 tyeOstring\ [email protected] )dnuggets1co [/#rray\[/SetPredicate\

[SetPredicate idOs003 :eldOtransaction oeratorOsuerset&\[#rray nOF tyeOstring\ roducts1.tl [/#rray\ [/SetPredicate\

[SetPredicate idOs00 :eldOtransaction oeratorOsuerset&\[#rray nOF tyeOstring\ !as)et1.tl [/#rray\ [/SetPredicate\

[SetPredicate idOs00Q :eldOtransaction oeratorOsuerset&\[#rray nOF tyeOstring\ c.ec)out1.tl [/#rray\ [/SetPredicate\

[Se?uence idOse?00F nu!er&SetsOF occurrenceOZ0 suortO01Z0\[Sete&erence set+dOs00F/\ [/Se?uence\

[Se?uence idOse?002 nu!er&SetsO occurrenceO0 suortO010\[Sete&erence set+dOs002/\[Deliiter deliiterOacrossTieindows

gaO&alse/\[Sete&erence set+dOs003/\[Deliiter deliiterOsaeTieindow

gaOtrue/\[Sete&erence set+dOs00/\[Deliiter deliiterOsaeTieindow

gaO&alse/\[Sete&erence set+dOs00Q/\ [/Se?uence\

[Se?uenceule idOrule00F nu!er&SetsOQ occurrenceO20 suortO0120

con:denceO012Q\[#ntecedentSe?uence\[Se?uencee&erence

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PMML Consuers

Post-Processing >isualiation >eri:cation and Evaluation Deloyent 6y!rids and Meta-Learning

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PE#" Post-Processing #ssociation

ules

Sets o& #ssociation rules are !rowsed li)e we!ages

PMML-&oratedassocationrules can !euloaded

,orge et al1(2002

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>ii - PMML >isualiation

,ava #let

Soe non-standarde5tensionsre?uired &or

!estvisualiation ettsc.erec)(

2003

eads( visualies and writes PMML:les

Couling wit. EK# in rogress

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Cn H >isualiing C

gra.s coare and evaluate odels

,ava #let %nderstands PMML

as an e5tension to

>ii

;arrand and ;lac. $.tt"//www1cs1!ris1ac1u)/

bYE&arrand/rocon/inde51.tl'

%se eceiver erator C.aracteristics $C' to

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Suary

Standards .el to strealine e@orts Sign o& aturity in :eld o& KD ;ro #rt to Engineering Standards are still incolete( !ut"

Use what is available!

More tools utiliing standards are

needed

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e&erences *rossan( 1L1( 6ornic)( M1;1( Meyer( *1 $2002'1 Data Mining Standards Initiatives(

Counications o& t.e #CM( >ol1 Q"Z see also .tt"//www1dg1org C.aan( P1( Clinton( ,1( Ker!er( 1( K.a!aa( T1( einart( T1( S.earer( C1 and irt.( 1 $2000'1

CRISP-DM 1.0: Step-by-step data mining guide( C+SP-DM consortiu( .tt"//www1cris-d1org Cli&ton( C1( T.uraising.a( 91 $200F'1 Emerging standards for data mining1 Couter

Standards W +nter&aces >ol 23 FZY H F]31 Compare and Contrast !"#P and $M" for #na%ysis

.tt"//www1ess!ase1co/resourceli!rary/articles/4ola5la1c& ,CA .tt"//www14c1org/en/4sr/detail7idO0FX ,L#P .tt"//www14c1org/en/4sr/detail7idOX]

,orge( #1( Poas( ,1 and #evedo( P1 $2002'1 Post-pro&essing operators for bro'sing %arge sets ofasso&iation ru%es1 Proc1 Discovery Science 021 $eds1 Lange( S1( Sato.( K1 and Sit.( C1 61'(L!ec)( *erany( L<CS( 2Q3( Sringer->erlag1

;arrand( ,1 and ;lac. P1 $2003'1 R!C!n: a too% for visua%ising R!C grap(s1 See".tt"//www1cs1!ris1ac1u)/bYE&arrand/rocon/inde51.tl

Melton( ,1 and Eisen!erg( #1 S)" Mu%timedia and #pp%i&ation Pa&*ages +S)",MM(.tt"//www1ac1org/sigod/record/issues/0FF2/standards1d&

M* Coon are.ouse MetaModel .tt"//www1og1org/cw/ S#P .tt"//www1w31org/T/S#P/ Tang( 1( Ki( P1 ui%ding Data Mining So%utions 'it( S)" Server /000(

.tt"//www1dreview1co/w.iteaer/wid2]21d& ettsc.erec)( D1( ,orge( #1( Moyle( S1 $to aear'1 Data Mining and De&ision Support

Integration t(roug( t(e Predi&tive Mode% Mar*up "anguage Standard and isua%iation inMladenic D( Lavrac <( 9o.anec M( Moyle S $editors'" Data Mining and Decision Suort"+ntegration and Colla!oration( Kluwer Pu!lis.ers1

AML# .tt"//www15la1org/

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