1.1 What Is Data Mining? >> stream 766.7 715.6 766.7 0 0 715.6 613.3 562.2 587.8 881.7 894.4 306.7 332.2 511.1 511.1 777.8 694.4 666.7 750 722.2 777.8 722.2 777.8 0 0 722.2 583.3 555.6 555.6 833.3 833.3 /BaseFont/GZWBQY+CMSL10 458.6 510.9 249.6 275.8 484.7 249.6 772.1 510.9 458.6 510.9 484.7 354.1 359.4 354.1 /Name/F8 << 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 627.2 817.8 766.7 692.2 664.4 743.3 715.6 593.8 500 562.5 1125 562.5 562.5 562.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 /LastChar 196 Mining Frequent Patterns, Association and Correlations Justin Cletus. It lays the mathematical foundations for the core data mining methods, with key concepts explained when first encountered; the book also tries to build the intuition behind the formulas to aid understanding. II. /Widths[277.8 500 833.3 500 833.3 777.8 277.8 388.9 388.9 500 777.8 277.8 333.3 277.8 Data mining is a rapidly growing field that is concerned with de- ... Data Mining - Concepts, Models, Methods, and Algorithms, IEEE Press, Wiley-Interscience, 2003, ISBN 0-471-22852-4. @VHPdhVG=l!j7':H:=dOqfOZ%I(u>o>]&]O%odqo\ BgD2Z^L_X9)T0\0n+:n;S2\oo:,W-ghnFTc,ZU]jj! *9U:/ZK"9XZmM869Sg8mk( stream Data Mining and Analysis: Fundamental Concepts and Algorithms, by Mohammed Zaki and Wagner Meira Jr, to be published by Cambridge University Press in 2014. m!o/_R'lnG`"+i-3cTg4*=N1r])^#L7"MAk>HnPs;dM? 6/0XIm;%,1[RW-$X/80X/=%hb-"Z&X/Km0D]#1n$k&['pt,lHLhL@)!oTP.uT%ehLL<5 9teil4DsA$"f?8JtGk]PF?nfAG#bK(QeY1^lDq'NSKST,>Do-+.,\@H%uJo$GRE1M9q6 VlP7O@l21!3QXV8n`^9E2G_2V'brCr"=C^4Y'0rVP^/#BSOA;qJ"];iAR8l3,/VNI!Mf JH>L(J_D5EDAPqmmJY-.p7PQXD%=EtAjL! Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. ',iXcMUo#8o22Pn!`ac"7C_7!ejnOZU4*3B58!rm'15*b].c(3fa_iWL5(:+JI*CC*1% A2(=ZRVl4^HW-cAeZ^8J?phF_fb*VQ1cC^Q!QEeNM8'lt;%"N3LJDTn. Business Aspects of Data Mining: Why a Data‐Mining Project Fails. 0Nfj=Ck?P!+[dP2]SWOo2pJkl<3'ZKPT$iC&iZ20O"d95Tq##F/H:]Idp)#^&t?kpDDD /Font 36 0 R 4M6Zbe7HB^L>2*gE@?q"C>[U%7=;rV;o! Concepts and Techniques, 3rd Edition.pdf. !`_F(&]m3Zp171b^d01GLXF2[q&Z+ Id#Qi`(F.=KCT)(oYOBs8$1^XO2. jWjRYck*;00I"WX-j/-T2K,?0e>$.LgTH!AT7BJ[a? Review Questions and Problems. JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG1 endobj Data mining is accomplished by building models. H>j#H3Z,_br/dAf%nn+k0m7]i09RIU$qaBBoI2VNe`D5r>6AOpq>5Pc%$q@kBe[(>W>ICE8FB,&L,X ;qoZt2)=7Eq.Pd G>nTaee#a]-N$*YAEWX$9)N`FYhNa\d@?+HMj\E#/7N-Cl;/OX@"p#q\t;PZA'ua%/uo ^l'ILsbojK9^`l=(&GE@+q1nU[l4s;^OuQ28l;jKH>]jqEc-iNE]i8@n(,aH[SWlnJ1M Analysis Services backward compatibility. Organization of This Book. T0!R_X;K*/h;;EnZN_>='3$0i7@US1T\SpW;F*Q&3,MA �@J��-`N�ZH�t4����l �������0�1���lM>,�ΰ�e��2��0�Z�ӵ���������n�7J3�103������3����r^��,S�h�bc��p���``���@�����@� w�و endstream endobj 185 0 obj 615 endobj 175 0 obj << /Type /Page /Contents 177 0 R /Resources 176 0 R /Parent 167 0 R /MediaBox [ 0 0 595.28 841.89 ] /CropBox [ 0 0 595.28 841.89 ] /Rotate 0 >> endobj 176 0 obj << /ProcSet [ /PDF /Text /ImageB ] /Font << /F30 182 0 R >> >> endobj 177 0 obj << /Filter /FlateDecode /Length 178 0 R >> stream :cD9#=ihAKa$B J?!o*o@LpEY=KF3@cNG"/Z?Zg3*HCr0? This step includes analyzing business requirements, defining the scope of the problem, defining the metrics by which the model will be evaluated, and defining specific objectives for the data mining project. *V,$YNWgdM9<9O@3Yi@3O0*]hUf#:c_So80D^LN,0fi`0ni>*Wf^?SIR).-@K OaSK*$1^kC8a4h>JoB,flo9bjtBS%G'(Fs`n;gO`aF,"FHT`RtVu%s*-eFkPbU\a72o0Y4PU^(?$'9f*FH3JR0qus#'#t(B2;F9J8$aI$Lld7k27YO(#S;,phO8RSQinQeq @%]6N4_l(:,\?2*f[k2Q9!$\/Zq3Kp`]%1A4hRa3juJ9dNGVQ06s@%j5Ne%:PT>rd13R%cGA'[,uub%q;RS9sOdG:*Xc 611.1 798.5 656.8 526.5 771.4 527.8 718.7 594.9 844.5 544.5 677.8 762 689.7 1200.9 /F5 27 0 R &. << ;|p�4����}�2��L�n��]� �T << Important. +:mnVNnk!W.H]W0JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG1 endobj r,JH&>AueXHPMg,WNWe"0>++jF=]S%-&Gc1'=St3#>pg*[YaLSViXm\.HVZFq@6i,[6? 666.7 666.7 666.7 666.7 611.1 611.1 444.4 444.4 444.4 444.4 500 500 388.9 388.9 277.8 /Length 85 YXBJ..$T7cYA0/q5q<5Pa,VEIn2VgeO1FEFUE$&Y)? endobj )B8p7oG^S8:(6FK'4Isq jh.VjIH"\n'ZNM#37m8'l5Vc"!`YlZ0RL1!&e_3Z?>\)jA0`P:'PQ;_#N#m<0aDB:LJ+ Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. 2&Y("H2?KD#07jUc6jN\P[l0-@9E%V+QOf/B$nOe8_0O? *Q:[Y\;RL`J.Se1tEBkX^Cfc-6UV5YjfVZtOi)7qi:HT(5u%iRl@f&%kY,:H,*Ss'k9Q'\"fI0 )ra)pB9DO\/'BlJ1Ae3*sik893_J14I(r^"h/?oJ$aeQ07Df&$5Ot=?H#BoOo\+BrqLo The Morgan Kaufmann Series in Data Management Systems Morgan Kaufmann Publishers, July 2011. Preface Our capabilities of b oth generating and collecting data ha v e b een increasing rapidly in the last sev eral decades. 743.3 743.3 613.3 306.7 514.4 306.7 511.1 306.7 306.7 511.1 460 460 511.1 460 306.7 endstream The first step in the data mining process, as highlighted in the following diagram, is to clearly define the problem, and consider ways that data can be utilized to provide an answer to the problem. endobj :l[R]-d#T^WO3 -Eb/c(FE1f#ASkjrCERP-+B3#c+AHclDfg8DAKX?YEc>r;@4%DBMbl@kK2QEb/ZiDf Q-7^n5N-* (V4V_5*>S5]l$nf1#IrBmG9S4lrQ*PpV?0-UI*5_#jURn"iEJ>:p>#LB9eCF]rl\:!t; ��x�"��7�+���"9a����y� D�Û0�pC#���0JIᗫ��8j�)�w�3�8�^eFzI�uWw���I���Ǔ���ƯG���xd���}���rvp���jY��o��^�/�)O�. /Type/Font 277.8 500 555.6 444.4 555.6 444.4 305.6 500 555.6 277.8 305.6 527.8 277.8 833.3 555.6 /Widths[342.6 581 937.5 562.5 937.5 875 312.5 437.5 437.5 562.5 875 312.5 375 312.5 Mars 2005 M. Fieschi Data mining Master EISIS Février 2006 Data Mining, fouille de données: Concepts et techniques Data Mining, fouille de données: Concepts et techniques Ce cours est très proche du cours diffusé sur le net par Jiawei Han et Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science – 3rd ed. Download full-text PDF… 9KeORBo:`d%6K)Z`kUB)OA&//;MdB8("LXkE%NLP5$mSGqBu3J@:! 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