María del Rosar io Bruer a. I BM Scholar s Pr ogram. Pregunt as y respuest as

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1 Dat a Mining María del Rosar io Bruer a I BM Scholar s Pr ogram Pregunt as y respuest as Pr egunt as: &XiOHVHOYDORUGHORVFOLHQWHV" &XiOHV VRQ ORV FOLHQWHV TXH WLHQHQ PD\RU SUREDELOLGDGGHGHVHUWDU" &XiOHV VRQ ORV SURGXFWRV TXH VH YHQGHQ HQ IRUPDFRQMXQWD"«Respuest as: (VWiQHQORVGDWRVGHOXVXDULR 6HQHFHVLWDQKHUUDPLHQWDVHVSHFLDOHVSDUD HQFRQWUDUODV

2 Business Int elligenc e S (V XQ SDUDJXDV EDMR HO TXH VH LQFOX\H XQ FRQMXQWR GH FRQFHSWRV \ PHWRGRORJtDVFX\DPLVLyQFRQVLVWHHQ PHMRUDU HO SURFHVR GH WRPD GH GHFLVLRQHVHQORVQHJRFLRVEDViQGRVH HQ KHFKRV \ VLVWHPDV TXH WUDEDMDQ FRQKHFKRVµ +RZDUG'UHVQHU *DUWQHU*URXS B.I.: rec ursos y herram ient as S)XHQWHV GH GDWRV ZDUHKRXVHV GDWDPDUWVHWF S+HUUDPLHQWDV GH DGPLQLVWUDFLyQ GH GDWRV S+HUUDPLHQWDV GH H[WUDFFLyQ \ FRQVXOWD S+HUUDPLHQWDVGHPRGHOL]DFLyQ'DWD 0LQLQJ

3 Qué es Dat a Mining? (1997) 'DWD 0LQLQJ es el pr oceso de explor ación y análisis - de maner a aut omát ica o semiaut omát ica - de los dat os par a obt ener pat r ones signif icat ivos y r eglas de negocio. 0LFKDHO%HUU\*RUGRQ/LQRII 'DWD0LQLQJIRUPDUNHWLQJVDOHV DQGFXVWRPHUVXSSRUW :LOH\86$ Reflex iones (2000) S QRV JXVWD OD QRFLyQ GH TXH ORV SDWURQHV GHEHQVHUVLJQLILFDWLYRV«S 6L KD\DOJR TXH UHFKD]DPRV HV OD IUDVH SRU PHGLRV DXWRPiWLFRV R VHPLDXWRPiWLFRVµ QR SRUTXH QR VHD FLHUWR VLQ DXWRPDWL]DFLyQ HV LPSRVLEOH PLQDU JUDQGHV FDQWLGDGHV GH GDWRV VLQR SRUTXH HQWHQGHPRV TXH VH KD SXHVWR GHPDVLDGR pqidvlv HQ OD DXWRPDWL]DFLyQ \ QR VXILFLHQWH HQ ODV HWDSDV GH H[SORUDFLyQ \ DQiOLVLV S 'DWD0LQLQJHVXQSURFHVR! "$# %'&( # ) * %+,- /

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6 Fuente: Mining your Own Business Data Using DB2 Intelligent Miner for Data Et apas en el proc eso de Dat a Mining,GHQWLILFDUHOSUREOHPDGHQHJRFLR 7UDQVIRUPDU ORV GDWRV HQ LQIRUPDFLyQ $FWXDUDSDUWLUGHORVUHVXOWDGRV 0HGLUORVUHVXOWDGRVGHODVDFFLRQHV

7 The Mining Pr ocess Fuente: Mining your Own Business Data Using DB2 Intelligent Miner for Data El Analist a de Dat os S(V HO YtQFXOR HQWUH ODV iuhdv GH WHFQRORJtD LQIRUPiWLFD \ ODV iuhdv GH QHJRFLRV S7UDGXFH ORV UHTXHULPLHQWRV GH LQIRUPDFLyQHQSUHJXQWDVDSURSLDGDVSDUD VX DQiOLVLV FRQ ODV KHUUDPLHQWDV GH PLQHUtD S5HDOLPHQWDHO'DWD:DUHKRXVHGHOD FRPSDxtDFRQQXHYRVFULWHULRVGHGDWD FOHDQLQJ\GDWDYDOLGDWLRQ

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10 Cost os de proyec t o Fuente: Mining your Own Business Data Using DB2 Intelligent Miner for Data Origen de los dat os S%DVHVGH'DWRV5HODFLRQDOHV S'DWD:DUHKRXVHV S'DWD0DUWVDQG2/$3 S2WURVIRUPDWRV([FHODUFKLYRV $6&,,HQFXHVWDVGDWRVFHQVDOHV HWF

11 Tipos de fuent es de dat os S7UDQVDFFLRQDOHVHMODVRSHUDFLRQHV UHDOL]DGDVFRQWDUMHWDGHFUpGLWR S5HODFLRQDOHVHMODHVWUXFWXUDGH ORVSURGXFWRVTXHRIUHFHHO%DQFR S'HPRJUiILFRVHMFDUDFWHUtVWLFDV GHOJUXSRIDPLOLDU La form a de los dat os para Dat a Mining S6HRUJDQL]DQHQIRUPDGHXQDWDEOD SODQD FRPSXHVWD SRU ILODV \ FROXPQDV S/DV )LODV XQLGDG GH DQiOLVLV3RU HMHPSORXQDFXHQWDXQWLFNHW S/DV FROXPQDV ORV DWULEXWRV GH FDGD XQLGDG GH DQiOLVLV3RU HMHPSOR IUHFXHQFLDGHXVRGHODWDUMHWDGH FUpGLWR

12 Carac t eríst ic as de las t ablas de dat os para Dat a Mining S7RGRVORVGDWRVGHEHQHVWDUHQXQDVROD WDEODR YLVWDµGHOD%DVHGH'DWRV S&DGD ILOD GHEH FRUUHVSRQGHU D XQD LQVWDQFLDUHOHYDQWHDOQHJRFLR S/DV &ROXPQDV VLQ YDULDELOLGDG GHEHQ VHU LJQRUDGDV S/DV &ROXPQDV FRQ YDORUHV ~QLFRV SDUD FDGD FDVR GHEHQ VHU LJQRUDGDV 1UR GH FXHQWD La c alidad de los dat os (O p[lwr GH ODV DFWLYLGDGHV GH Dat a Mining VH UHODFLRQD GLUHFWDPHQWH FRQ OD CALI DADGHORVGDWRV 6H GHEH LGHQWLILFDU ORV GDWRV IDOWDQWHV missings RIXHUDGHUDQJR out lier s

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14 Preparac ión de los dat os `ba cta dea f$ghi jklg m gn go$ctgp `ba cta 7UDQVIRUPHG'DWD ([WUDFWHG,QIRUPDWLRQ $VVLPLODWHG,QIRUPDWLRQ 6HOHFW 7UDQVIRUP 0LQH $VVLPLODWH Dat a Warehouse S'DWD :DUHKRXVH LV D VXEMHFWRULHQWHG LQWHJUDWHG WLPHYDULDQW QRQ YRODWLOH FROOHFWLRQ RI GDWD LQ VXSSRUW RI PDQDJHPHQWGHFLVLRQV %LOO,QPRQ S$FRS\RIWUDQVDFWLRQGDWDVSHFLILFDOO\ VWUXFWXUHGIRUTXHU\DQGDQDO\VLV 5DOSK.LPEDOO

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22 Cust om er Relat ionship Managem ent S(V HO SURFHVR TXH DGPLQLVWUD OD UHODFLyQ HQWUH OD FRPSDxtD \ VXV FOLHQWHV S3DUD TXH UHVXOWH H[LWRVR UHVXOWD QHFHVDULR LGHQWLILFDU ORV SDWURQHV GHFRQVXPR\FRPSRUWDPLHQWRGHORV FOLHQWHV Dat a Mining - CRM S'DWD 0LQLQJ VH XWLOL]D SDUD VLVWHPDWL]DU ORV SURFHVRV GH E~VTXHGD GH ORV SUHGLFWRUHV GH FRPSRUWDPLHQWR GH ORV FOLHQWHV HQ ODVHWDSDVGHGLVHxRGHFDPSDxDV S7DPELpQ VH DSOLFD SDUD OD PHGLFLyQ GHORVUHVXOWDGRVGHODFDPSDxD\OD UHDOLPHQWDFLyQGHO&50

23 Problem as t ípic os de Dat a Mining S&ODVLILFDFLyQ S(VWLPDFLyQ S3UHGLFFLyQ S$JUXSDPLHQWRDSDUWLUGH UHJODVGH DVRFLDFLyQ S&OXVWHULQJ S'HVFULSFLyQ\YLVXDOL]DFLyQHWF Problem a de Clust ering $JUXSDUDORVFOLHQWHVVHJ~QVXVLQGLFDGRUHV 55HFHQF\ ))UHFXHQFLD 0 0RQWR HWF HQVHJPHQWRVGHFRPSRUWDPLHQWRKRPRJpQHR 5HVXOWDGR &OLHQWHV +HDY\ 0HGLXP /LJKW HWF (OGHODIDFWXUDFLyQVHFRQFHQWUDHQHO FOXVWHU+HDY\GHORVFOLHQWHV /RV FOLHQWHV +HDY\ VRQ FDVDGRV FRQ KLMRV WUDEDMDGRUHV DXWyQRPRV FRQ XQ LQJUHVR VXSHULRUD

24 Problem a de Clasific ac ión &ODVLILFDU XQ QXHYR FOLHQWH GH DFXHUGR D VX SHUILO VRFLRGHPRJUiILFR FRPR SRWHQFLDO FOLHQWH+HDY\0HGLXP/LJKW Problem a de Est im ac ión (VWLPDU HO FRQVXPR GH XQ GHWHUPLQDGR UXEUR GH DUWtFXORV GH XQ JUXSR FOLHQWHV HQ HO SUy[LPR WULPHVWUH (VWLPDU HO /79 /LIH 7LPH 9DOXH SRWHQFLDOGHXQQXHYRFOLHQWH

25 Problem a de Predic c ión 3UHGHFLUHODEDQGRQRGHXQFOLHQWH FKXUQLQJDWULWWLRQ 3DUDXQDFRPSDxtDGHWHOHIRQtD FHOXODU 3DUDXQD$)-3 3DUDXQDWDUMHWDGHFUpGLWR Problem a de Asoc iac ión (QFRQWUDUODVUHJODVTXHGHWHUPLQDQ HOFURVVWUDIILFHQWUHSURGXFWRV SDUDORVFOLHQWHVGHXQ%DQFR3RU HMHPSOR &XDQGRXQFOLHQWHVHDFWLYDHQ&DMD GH$KRUURVHOVLJXLHQWHSURGXFWR HQGRQGHVHDFWLYDHV3UpVWDPRV SHUVRQDOHV(VWHSDWUyQRFXUUHHQ HOGHORVFDVRVµ

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27 Problem as usuales S3URJUDPDVGHPLOODMH\ILGHOL]DFLyQ S&RQVROLGDFLyQGH%DVHVGH'DWRV SURSLDVFRQIXHQWHVH[WHUQDV S:HEPLQLQJ\DQiOLVLVGHWUiILFR\ XVRGHUHFXUVRVGHHEXVLQHVV S'HILQLFLyQ GH PDUFRV PXHVWUDOHV SDUD LQYHVWLJDFLRQHV GH PHUFDGR \ HQFXHVWDVGHFXVWRPHUVDWLVIDFWLRQ La elec c ión del m odelo para Dat a Mining 3ULQFLSDOHVREMHWLYRVGHOSURFHVRGH'DWD 0LQLQJ pr edicción descr ipción (O PpWRGR D XWLOL]DU GHSHQGH GH ORV REMHWLYRVSHUVHJXLGRVSRUHODQiOLVLVSHUR WDPELpQ GH OD FDOLGDG \ FDQWLGDG GH ORV GDWRVGLVSRQLEOHV

28 Fuente: Mining your Own Business Data Using DB2 Intelligent Miner for Data Cóm o selec c ionar una pot enc ial aplic ac ión de DM &RQVLGHUDFLRQHVSUiFWLFDV 3RWHQFLDOLPSDFWRVLJQLILFDWLYR5HODFLyQ FRVWREHQHILFLR 1RKD\RWUDDOWHUQDWLYD ([LVWHVRSRUWHLQVWLWXFLRQDO 1RH[LVWHQLPSHGLPHQWRVOHJDOHVGHXVR GHODLQIRUPDFLyQ

29 Cóm o selec c ionar una pot enc ial aplic ac ión de DM Consider aciones t écnicas: 'LVSRQLELOLGDGVXILFLHQWHGHGDWRV 5HOHYDQFLDGHDWULEXWRV %DMRVQLYHOHVGHUXLGRHQORVGDWRV 3UHFLVDUHOQLYHOGHFRQILDQ]DSDUDORV UHVXOWDGRV &RQRFLPLHQWRDQWHULRUH[LVWHQWH La evaluac ión de los m odelos &XiQDMXVWDGRHVHOPRGHOR" (VFRUUHFWDVXGHVFULSFLyQGHORV GDWRVREVHUYDGRV" &XDQWDFRQILDQ]DVHSXHGHWHQHUHQ VXVSUHGLFFLRQHV" &XiQFRPSUHQVLEOHHVHOPRGHOR"

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35 Problem as de Tex t Mining S$QiOLVLV GH FRQWHQLGRV GH SiJLQDV :HE S2UJDQL]DFLyQ GH VHUYLFLRV GH E~VTXHGDHQOD:HE S([WUDFFLyQGHFRQFHSWRVGHVtQWHVLV HQ GRFXPHQWRV UHIHULGRV DO PLVPR DVXQWR Conc lusiones

36 Para qué Minería de Dat os S/D 0LQHUtD GH 'DWRV HV XQD KHUUDPLHQWDHILFD]SDUDGDUUHVSXHVWD SUHJXQWDVFRPSOHMDVGH,QWHOLJHQFLDGH 1HJRFLRV S/DV KHUUDPLHQWDV GLVSRQLEOHV SHUPLWHQ DXWRPDWL]DU SDUWH GH OD WDUHD GH HQFRQWUDU ORV SDWURQHV GH FRPSRUWDPLHQWRRFXOWRVHQORVGDWRV S3HUR«Qué no puede aut om at izarse (t odavía) S/D HOHFFLyQ GH ORV SUREOHPDV GH QHJRFLR FDQGLGDWRVSDUDWDUHDVGH'DWD0LQLQJ S/D LGHQWLILFDFLyQ \ UHFROHFFLyQ GH ORV GDWRV TXH FRQWLHQHQ OD LQIRUPDFLyQ EXVFDGD S(O PDVDMHR \ WUDWDPLHQWR GH ORV GDWRV TXHSRVLELOLWDODE~VTXHGDGHSDWURQHV S(OGLVHxR\FiOFXORGHYDULDEOHVGHULYDGDV

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44 E s ta d is tic a s Clu s te r ,0 0 % d e p o b la c ió n 'HVFULSWLYRVJHQHUDOHV S o lte Dro ivo rc ia d o /Viud o Ca s a d o s c io s e d a d e s ta d o _civil N o tra b acja ue nta P ro p ia R e la c io n d e p e n d e nc ia Ma s c ulino F e m e nin o S i N o o c up s e xo hijo s a vg tc kt fre c u p e s o s Crit erios de segm ent ac ión S6H WRPDQ FRPR YDULDEOHV DFWLYDVµ ODV TXH FRUUHVSRQGHQ DO FRPSRUWDPLHQWRGHFRQVXPR S6H WRPDQ FRPR YDULDEOHV VXSOHPHQWDULDV ORV DWULEXWRV VRFLRGHPRJUiILFRV

45 Credit Card 1 Masculino Femenino Divo So ltero rciado /Viud o Casado No Cuenta trabajapropia Si Relacio n d ep end encia No scios [se xo] [estad o_ civil] [ocup] [hijos ] fre cu pesos avgtckt [edad] 55 2 Divo So ltero rciado /Viud o Casado No Cuenta trabajapropia Masculino Femenino Relacio n d epend encia Si No s cios [estad o_ civil] [ocup] [se xo] pe s os [hijos ] fre cu [e da d] a vgtckt 27 0 Divo So ltero rciado /Viud o Casado No Cuenta trabajapropia Si Relacio n d ep end encia No Masculino Femenino 18 scios fre cu pesos [estad o_ civil] [ocup] [hijos ] [se xo] a vgtckt [e da d] Credit Ca rd Clus ter 2 Tienen 4 o más débitos automáticos Casados S o ltedivorcia ro do /Viudo Ca s a d o 27,21% de pobla ción Trabajo Cta Propia No tra b acue ja nta P ro p ia R e la cio n d e p e nd e nc ia s cios [e s tado_civil] [ocup] Ma s c ulino Fe me nino Saldo >>> S i No Varones Con hijos [s e xo] Uso frecuente Edad pe s os [hijos ] Ticket >>> fre cu [e dad] a vgtckt

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47 Algorit m os S&+$,'&KL6TXDUHG$XWRPDWLF 'HWHFWLRQ S& 57 &ODVVLILFDWLRQ DQG 5HJUHVVLRQ7UHH S&4XHVW\RWURV S,QWHOOLJHQW 0LQHU XWLOL]D XQD YDULDQWHGH& 57 Arbol de c om port am ient o Si tiene 4 o más débitos automáticos y un saldo > $ 727 entonces su probabilidad de pertenecer al cluster 2 es del 99%

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54 Reglas ŒU 6Ž R Œ Ž UŽ šœ W 6 Hongos Pepperoni Queso Hongos --> Pepperoni Hongos --> Queso Queso --> Pepperoni Hongos + Pepperoni --> Queso Hongos + Queso --> Pepperoni Queso + Pepperoni --> Hongos Pueden descartarse por bajo soporte Reglas significativas Ot ro ejem plo de MBA S/D DVRFLDFLyQ VH SODQWHD HQWUH ORV WRSSLQJV GH ODV SL]]DV \ ODV EHELGDV S/RV JUiILFRV GH UHJODV SHUPLWHQ YLVXDOPHQWH LGHQWLILFDU UHJODV FRQ EXHQVRSRUWHFRQILDQ]D\OLIW

55 Reglas Soporte (%)Confianza(%) Tipo Elevación Cuerpo de regla Cabecera de regla [Hongos]+[Otra bebida] ==> [Pepperoni] [Cerveza]+[Pepperoni] ==> [Hongos] [Cerveza]+[Queso] ==> [Hongos] [Hongos]+[Pepperoni] ==> [Cerveza] [Cerveza] ==> [Hongos] [Hongos] ==> [Cerveza] [Hongos]+[Queso] ==> [Cerveza] [Pepperoni]+[Queso] ==> [Gaseosa] [Hongos]+[Pepperoni]+[Queso] ==> [Cerveza] [Hongos]+[Gaseosa] ==> [Queso] [Gaseosa]+[Pepperoni] ==> [Queso]

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