Data Mining Data Mining

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Data Mining Data Mining

  1. 1. ¡  © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ Model Model Data Support Mining Support Mining Decision Data Decision Data DM, then DS DM DS in Data Pre-Processing Sequential Application: –Meta-learning and multi-strategy learning Model Model –ROC methodology Improving models by data analysis • MS Analysis Services • e.g.: MS OLE DB for DM • Supporting decisions in the DM process, Incorporating DM methods into DSS, e.g.: Support Mining Decision Data Expertise Data Support Mining Support Mining Decision Data Decision Data Models Decision Support for Data Mining Data Mining for Decision Support Integrating DM and DS through $0 0$ $0( ! 0$ !† … 0$ t ' s … ! 0$ !t ' s © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ U3`RcbR dR1VX €€€ u Decision Support 89@6I 56BQPv8PE9 FPD u A5B89@6I 9QvpBFQQPoBQ8vI 89@6I 56BQPv8PE9 FPD u ? C98vF 56BQPBD6CCP u dR1VX knfm elik gjihf fgfe @9CG8P5P CF9I6QCvD u CFPD H6 C9yGQ u Data Mining @9F9QCv8D CF9I6QCvD u x„€ƒƒ‚ {€z z ~}v |{z{zy w xwv PQP@ I6FH … … “’‘‰ˆ‡† ’†•“” ’”–‰ d™˜“— trshriq GF9E6DCB@ 9A@987654 ! ! u ! 0$ !t ' s 0 „$ƒ) ‚ ƒ  © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ U3Ued2W2 WVTUTSR1 aU3`RcbR aU`RY2X WVTUTSR1 dR1VX €€€ u Decision Support 56BQP5P8yx9 u 56BQPwB8PvCBE u A5B889@6I CBCG8P5P u 56BQPv8PE9 u !0))(' %$ # # ! A5BF9QCv8D u 2321 dR1VX 56BQPDBHBCCP8D u tgshriq pi hgf Data Mining PQP@ I6FH GF9E6DCB@ 9A@987654
  2. 2. ‡  © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ false positive rate false positive rate 100% 80% 60% 40% 20% 0% 100% 80% 60% 40% 20% 0% 0% 0% slope = 4 2 = 2 20% 20% Pos =4 40% 40% Neg 60% 60% true positive rate true positive rate FNcost 80% 80% 2 = FPcost 1 100% 100% 0$ ¾ % 0$ ¾ ƒ% ø $ % $ ƒƒ(ø ‚$ ù% ÂÁ $ø ÷ © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ False positive rate 100% 80% 60% 40% 20% 0% 0% 20% false positive rate ðïîí ñ ëê ÕÔÓ Ò 100% 80% 60% 40% 20% 0% 0% 40% ðïîí ì ëê ÕÔã Ò CN2 classifier 2 W RAcc classifier 1 §š” Çé ÎÈÊ èçæ à 20% 60% Confirmation rules True positive rate ÝÙ × ÙÛÚÙ × Þ ÕÔã Ò 40% 80% ÝÙå × ÙÛÚÙä × Ö ÕÔã Ò 60% 100% ¸ ˜’Ð Ñ ¸ §•³ ’§—š¥Ð true positive rate 80% Ï ÇÌ ÎÅ ÇÍËÌËÊÉ È ÇÊâ Îá à 100 70 30 50 50 0 Negative examples 50 20 30 Positive examples ÝÙà × ÙÛÚÙß × Þ ÕÔÓ Ò 100% Predicted negative Predicted positive 100 50 50 ÝÙÜ × ÙÛÚÙØ × Ö ÕÔÓ Ò Classifier 2 50 40 10 Negative examples 50 10 40 Positive examples Predicted negative Predicted positive ¸ §•³Ð Ñ ¸ §•³ ’´œ “Ð Classifier 1 Ï ÇÌ ÎÅ ÇÍËÌËÊÉ È ÇÆÅÄ Ã ƒƒ(ø ‚$ ù% ÂÁ $ø ÷ ƒƒ ø ÂÁ ø ÷ $% ) ÂÁ ÂÁ öõíôã ÕòóòÔ © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ §•ž§’‘ž– ‘’—¶•œ ³ “’œ ’¥¥ž– ˜žœ ’–ž§•©  ¼»º² ž ž¹¢£ ·¸ ˜¸ ’ · §—’–•‘ ”˜´•œ ”“ ˜žž¶‘•©  §’¨œ ´© Ž ·¸ ˜¸ ’ Œ±{€z xw ~z°ƒƒw °}°°w„wƒ® ‹v µ{w yv‰ ˆ §§’©•œ ³ Ÿ£ ’”“ ž”“žÀ “œ •³³´§ •ž§ž©’£ Ÿ£ œ •¥ ˜ž§§’©•œ ³›’œ ³ š“š– ž §—’–•‘ ˜ž§´  ¬ Œ±{€z xw ~z°ƒƒw °wz x{}‚¯} ® ‹yv {}­ x v‰ ˆ ”©š•œ ³³š –š² ›“¦›§•ž§ž©’£  ¬ Œ±{€z xw ~z°ƒƒw °wz x{}‚¯} ® ‹v {}­ x yv‰ ˆ «š“š– ‘•œ ¥ª •ž§ž¨’œ —’–•‘  §’©ž¨œ ’  §ž§™—š¦  Ÿ  Ÿ£ œ •¥ ¤£ ¢¡   Ÿ  Œ ‹v „€ Š yv‰ ˆ ˜žœ š’—›š“’‘  ™˜•—•–•”“’‘ Ž  x„€ƒƒ‚ {€z z ~}v |{z{zy w xwv Œ ‹yv „€ Š v‰ ˆ ' ' # ¿ 0 ¾ '½ ½
  3. 3. ú  © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ Model (Zupan Bohanec) —’–•‘ ’“´¶žœ ““š›ž“—´‘ —š©ž”©œ šœ ’ž” ’¨ž“š“ž—š´8 §™šÀ “’œ ’¥¥ž– ž –’³•—’¨’– §—’–•‘›¶´§ ˜žž¶‘•© û vqXt…„ Wƒ S‚ ¼»º² –š ¹¢£ ™¶ «§ª—’–•‘ ¥• “’‘³•—’¨’– —’——šœ š³ ûY qYY xr x S€ 65 43 7 21 “œ ’³a’ ’”“ ™¶ –’ž¥’œ ™—“’´y’§¶´§ —’–•‘ –’³•—’¨’–›¼»º² ûY xtr qh Sw •ž§ž¨œ ’³´§ “œ ’³a’ œ ’–´ š“š– ‘•œ ¥ û vqut sr qpi h Sg š“š– ‘•œ ¥ û `YX W fedc Sb x„€ƒƒ‚ {€z z ~}v |{z{zy w xwv Expertise Data ’§ž“œ ’³a’ ‘•œ ¥ û `YX W VUT SR Q@PIHG BFE D@ CBA@9 ©§©© ©¨§¦ ¦ „ ' „ ¿' # ½ © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ ! x„€ƒƒ‚ {€z z ~}v |{z{zy w xwv 0) $( ' % #$# ©§©© ©¨§¦ ù$Á ƒ$# „ ¿' 0 ¾ ½ Á ƒ # ¿ ½ „ ¿' # ½ © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ ëêïðï òÕ ôò¥ê¤ Ò false positive rate ðòõï Õòý ðïð£ôíë¢ ¡ Õò Õòý ÿþý ýü Ò 100% 80% 60% 40% 20% 0% 0% û §§’©•œ ³  £ ’”“ •“ž §–•”“’‘ Ÿ£ ˜ž©´–•œ “º slope = 4 8 = .5 20% Pos =4 40% Neg 60% true positive rate FNcost 80% 8 = FPcost 1 100% x„€ƒƒ‚ {€z z ~}v |{z{zy w xwv 0$ ¾ % 0$ ¾ ƒ% ø ¿' 0 ¾ ½ ½
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  5. 5. ” “B’‘@G % $ % „ %$# % ˆ „ ! 0$ !t ' s 0$ t ' s IQB“B˜BP—• – •P “BAH• – D@ Q@PI•PABG °€€­ ~ ž„wz„œ °€€­ ~ ­|zŸ #$# w¯} ® Œyv Œyv ­¬«Ÿ › š ™ › ¢ ¡   £ final achievement GA 1st grade c5 Û Õê Ø ¤ðòó틥íÕ¥ ¤ =1 1 c1 for lang 8th grade LEGEND: 2 Slovene gen ach 7th grade GA 1st grade - general achievement of the first high c2 school grade = 3 3 SSS Slovene - mark of subject Slovene language regular enrol History - mark of subject History GA 1st grade 5 for lang ß Õê å ¤ðòó틥íÕ¥ ¤¦ Physics - mark of subject Physcis age enrol - age at enrolment (in months) unex ab 3rd sem - unexcused absence in the third = 2 2 c7 c3 semester (hours) History 4 citizenshi p z ±°v Œv birth state ¥ò¥ëêôêՌ ¤§ = 2 2 c6 Physics 4 gen ach prim sch c4 = 1 1 math 8th grade age enrol unex abs 3rd sem phys 8th grade = 180 180 = 0 0 4 1 4 2 òóíô ðôïí© ¤ª ëêêð ðôïí© ¤¨ {€z x~zµ}„œ © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢ ’  ! ƒ ˆ ƒ ƒƒ  „ ! %ƒ)) ˆ ƒ$ƒƒ 0 ² ² 00 ƒ (ƒ% ˆ Œ}„w }­ ~w€„ƒƒw v ¸ yv Models for Building Construction Models for Client Value Project Attributes Building Feasibility £Õíóëò¤òôŒ¤êõ Ò £Õíóëò¤òôŒŒ‹ð Ò Decision Support Data Mining ˆ w}„w ­ ~„w} }„ |{zƒ€°} º}µ µ{w ¹}¬ ˆ Œ {€z xw{z»€ ~ °w ~zƒ ž­ Á¼ À¿¾ ½¼ Ò ½ À¿¾ Á Building Feasible ¼ ¼ Ò Designs to ½ ÆÅÄà ÂÁ Building ¼ ¼ Ò maximise Designs Á¼ ÆÅÄà ½¼ Ò Client Value ½ ÈÆÇ Á ¼ ¼ Ò ˆ Œ {€z x}‚¯ {}ƒÉ Value Feasible ëêïóíÕ¥òóëï ýÍÎüÍ ©ê Ì´Õê·ò¤íÕ©Ë ëêïóíÊïôí¤Õê© Ò Shape Zone Zone ðòöõíêՌŒí ¥ëí ðòï¥êôê¥êöóò¤ ëꤤêõ Ò ëêïóíÊï¥Õí¥ëíóð Ò lity Size ua Q ï´ð·êÕóð¶ õïÕµ ³õòëíöêˆ ê´Õíü ³òô£êü ò òóý © £¨ ¦§ ¦¥¤ £¢ © £¨ ¦§ ¦¥¤ £¢  

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