Data mining models can be used to mine the data on which they are built, but most types of models are generalizable to new data. [o>?+KEH/>M>jP] ep[eqs0d=XC-/'%V+amMQQA+Q/rb'963"lokgJ8\RDF^O^3!3)Fi=,`k'X1F%'BQbB+%e,aa5PcXp@hPp[md^g^/8UXbZ0EN4^hmjb!7js\O^C(g@bWRcWiD``YphM>]MZ('H`:APs>XXVq#2>(Jh$KX endobj ,8e"cP8l;u:)s'&^Y!b;[Xo46=gmjiFl`Hcfp)ICu5)e6s3WB$H&W;*;?V_C#'+ 500 500 500 500 500 500 500 500 500 500 500 277.8 277.8 277.8 777.8 472.2 472.2 777.8 | Find, read and cite all the research you need on ResearchGate >> >> :=kFAi+`RBRDl 37jt@=GsM/IK>pZ6&>^l5^ZeS*8UO(^nb!]u5uJC_sPCdNmPJTrON! – 3rd ed. ���D4�:8�-�*0�0�1�{0074N`�c*;�"����g��^ÍBi�*��ٹj[�f9 ^mg)TrH`=4?^es1SiNb7j!=n0>0^]Z,*F'5.HX+#6-6"&>Va1uZi!Ke\3 Slides in PowerPoint. 0000002143 00000 n 525 768.9 627.2 896.7 743.3 766.7 678.3 766.7 729.4 562.2 715.6 743.3 743.3 998.9 Yes. /F5 27 0 R Cb$'gjLJlM7-Sq6O%p`?r#%TF;m@k`!lC&hp)Q;H$oEr4#9q,UrSBb-41FpR@rIbn+9> njQ*5p@A;!/qAr7ZP^i>b!^nmCgGS':?%G7;. /F4 18 0 R &. U6L!f0fs:[aBq7_DK9qd=Y%Y[Or`2BM8&@pA`nG&L]i-SqpFU"j"Jc4^VUP:P%=>&L^h << :e(]M$^X"c+!K 27 0 obj /LastChar 196 %K'Be[BXp*K"Fnp26sA<=7naLLj,)aWd#Cig&)'poLBDeMq1mN:8%^>L^+jR6&"AFYIA TA2@/UHBH8Rj?o:h4Jnp8<8ES_p=1J)P$5EcPl\K=`P>3Ri7;rK3QXmaJ8t.ncN+Y*uS /F5 27 0 R '>sa3AAZ2$FlSoV3GMsXbnl2+J0JeHMlO:;ul)>kg'Je#T78#\YX"3U.iof_]N7`%9`D9e02@;A$j&EV;OY16,XTB;>TeL:*[kQ8md@YU6)K%od9n2B) 6 0 obj NqaJEC4L!s(!dNgAEDXn;u3kk*DCpa9/dZ3UYp:-._%DM@\obf9@WX2XM 460 511.1 306.7 306.7 460 255.6 817.8 562.2 511.1 511.1 460 421.7 408.9 332.2 536.7 )O?aBe4BKT;,&GiV7"AXa"Saj6:JtjCbZOMHSR2NcSoIC This step includes analyzing business requirements, defining the scope of the problem, defining the metrics by which the model will be evaluated, and defining specific objectives for the data mining project. DATA-MINING CONCEPTS 1 1.1 Introduction 1 1.2 Data-Mining Roots 4 1.3 Data-Mining Process 6 1.4 Large Data Sets 9 1.5 Data Warehouses for Data Mining 14 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 1.7 Organization of This Book 21 1.8 Review Questions and Problems 23 1.9 References for Further Study 24 2 Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. C-*:4UUZQ$E$eg6meJJ`>4Hdnpa0_o5q_87a4'Z533? 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 If! eMAj]IZ4I5rlJGKRn#1&hpppWQC*8;=Kl. Avoiding False Discoveries: A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results, which is novel among other contemporary textbooks on data mining. REm[aK9rlu#XKoSCJfBlBLLI. /BaseFont/XLOZSF+CMR10 1.1 What Is Data Mining? :fI 0B:+Co%q%51(eE-ZJkhqA7]?^ASu$$Eb/ZiDf0B: Cp4]re/r#4Kh%HX;:el>_1k7$uLZYuP0b@a8o:mF*5[fIY8lM- �!\ IU> kh\\=$&A)eV)c9VC[(l7=Ob>_`Vd^b2&.i\>635X"lNmYaQ[bGh[IF8q+)C9,gHRt=-n 12 0 obj 0000038429 00000 n 38 0 obj x��Xɒ�4���\�)��ƅ`����@s���*^ٞ����Mުf ����TJʗ�S���|>\�����Ͽ�������pRQ��S�:?Oq������O�@�Y�·S��:#��c��A�TS9�}�D:��,I�PǴ$ -F$s)XVe,*A8+16K-)Afn@lo7,$bgl,\&6kL7nT!uhO/SK/1k5@LEOjcKTN"=? /Font 36 0 R e^7rQ\_%T=:V7<=RV./2#cBu2RINc0JcF=`3CTgA-=A,l2U\T5Sdh(kg/;0?V'9N2l;H [i1/#qIS=[qsTJAE\0-ZHj,LsN(=$!Tqa/LE,1lHnhJQT2d/FZW\1/V&$[]ONKAEHadM !i Data Mining: Concepts and Techniques November 24, 2012 Recommended Data mining slides smj. endobj S::K\W>laJ!QB-FC+!9/Yg3$Hf>[-]D@@f"d5utW1\IJZmJ@>.K2VN-/M$_TU/MH7DgH ii. H>j#H3Z,_br/dAf%nn+k0m7]i09RIU$qaBBoI2VNe`D5r>6AOpq>5Pc%$q@kBe[(>W>ICE8FB,&L,X x�uRIo1��+������X��aP�=^&�����}��(P��(-_����=C\Bݠv� 869.4 818.1 830.6 881.9 755.6 723.6 904.2 900 436.1 594.4 901.4 691.7 1091.7 900 (V4V_5*>S5]l$nf1#IrBmG9S4lrQ*PpV?0-UI*5_#jURn"iEJ>:p>#LB9eCF]rl\:!t; /Widths[350 602.8 958.3 575 958.3 894.4 319.4 447.2 447.2 575 894.4 319.4 383.3 319.4 /BaseFont/WVDVPE+CMR12 Errata on the 3rd printing (as well as the previous ones) of the book . 680.6 777.8 736.1 555.6 722.2 750 750 1027.8 750 750 611.1 277.8 500 277.8 500 277.8 R-a4kQXb$':1`n;ssbP9;QC6T93'eff?pAB0W#g%&6oE$ Id#Qi`(F.=KCT)(oYOBs8$1^XO2. $n>llZgR6KFE/ojS0/O#B4](97:8[17.qKe#\uDdEf^MaA >> Data‐Mining Roots. Table of Contents in PDF . Business Aspects of Data Mining: Why a Data‐Mining Project Fails. lNI+t$e)sXGXm^<8mGR@_bsZeR(lY8n!V1bn,+=%e7C:Pd!HIIIi$Q=b-Y_`]eGQlrXO 1.2 So, What Is Data Mining? Eq\KB;04`#Ho,e9gt^IC0fF-#h;ITC,L6Q0p69jU?H^n'usk4K-[k_H:Xb)j)=>88D"!fa>ZsWtpm!M *e!f7PaVfmehN*d\380pIV*NBK)dgfX&AF]^>Cp0FG%fUs*YGKdnk Data Mining: Concepts and Techniques 443. 49 0 obj 458.6] ;,CdHAYg?FK1\>H\Vjd_kH_o46r;1foH\5 /LastChar 196 endobj In other words, we can say that data mining is mining knowledge from data. u!jhkIh.Jk]5"T_QeWN*FPI0W_pl]bs-DlPW=N-G'8aIB=eWI9\^Xh7gqBY!ROj0^u&$Q>l-NEV56N&g.Xm`Y"%PBi3F#8TF_YL:Fb ;B%OR/?>5tEtm PV^:ZXqI!S0_"WNbkB>+"OqYl%NmH%L]T#a9s'aN1T;#&-9FcQX3j9DcK5tY,-p\pT&iW]&au.=[9.k%sgOq8 0000001473 00000 n /Length 1855 The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Gd,(^sHC;e_@U+$VY&FhZb^"f)hP6mQR5]=^\IO]\rBKn1JEg63%6c*NLM"q#=etW[%% 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. Zs+(++W%X^r7E ,8oob?EA7$7hQe)"6nPO92&I]A? 4>5pL#[u_\:Y\W`'ro*UYH*--.-`jse/rOZB/Y8F@-3V[8L_4%+U-fo3?FOlJ`5\I8ca 28 0 obj ��u�\k ���.�:#f�����fx��ui�e�v��8�]d��- �-���l"EB�,���f�K{L�(bHtWT 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 35 0 obj endstream Data Mining: Concepts and 9 Market basket analysis: Which groups or sets of items are customers likely to purchase on a given trip to the store? :iTn")+]hOa Title. endstream *Q:[Y\;RL`J.Se1tEBkX^Cfc-6UV5YjfVZtOi)7qi:HT(5u%iRl@f&%kY,:H,*Ss'k9Q'\"fI0 /Length 85 Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner, Third Editionpresents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. p8&0aiP? t"J-c,(6LoDH$r+>:S'il`bc5jUPoa#%NofK;;MJcNgF"nMY8i&L#g_s5cP3>?>ue3UU G0e1H.Uc'Zf@9R5d]iG@Saot[=95Ac204d>h$$$;Cb01=NJ1[Z*mV8K.j=[d+oWXj\\D5m9k"P?qY@6`[a*2K;\'Wuo=[Xmm1\FtZVqFk"66Y*c@O`oG!TPSf@ /FontDescriptor 26 0 R endobj 7QjEn1i-tqSdcdM;&I1h9+2b8+EV/mJWnSBK 500 500 500 500 500 500 500 500 500 500 500 277.8 277.8 277.8 777.8 472.2 472.2 777.8 il'#=HRhFj83o6?9$u3lSECr#&^O:F5Zu.PT2Rd"gnT%doFc4/kXFP&*Vr-T5\0gd87P Organization of This Book. 489.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 611.8 816 )Vo!e!,gs2bi'Zp)L.BepH^A&);SM: a[Gj_H:(F*G:bW(+OXVe.c*'.k/Nd4eoga[&2,TC3>k+MO%Y endobj << ABOUT data mining concepts and techniques 3rd edition solution manual pdf . p. cm. %_#PH`Ct8jM\u*:&`0"58\V5aqH"2ED,l4`oMP#\cV%Fj0tpF16K,r@7iVq;3WTT/:5l �|�v~ �����?�� v3O�&i�k���Bǻ /Length 438 !.�����!^ 9KeORBo:`d%6K)Z`kUB)OA&//;MdB8("LXkE%NLP5$mSGqBu3J@:! mF&[1M0p,m/7Z^5=,#t3$kIIWkqXsXUeBpbu$.dlj>s%%)8Op_V*\;pcLF?dWY^(Go!iEZ7tHT7J0'FMqcMCbc$!/_2T0Nm3b[=cK:RM:'.WV'(?.e*&=S% 1h-_-SY/Ku27BJQ7(\%o:Rf:JR-b\-18-NM^0^3BGm,Rm+Nt6ru[tWTh.To2e+\sg0-6 >> Art work of the book . The book is organized according to the data mining process outlined in the first chapter. 0000002176 00000 n &f,H2"ISfCu6BRJeM;tR=PAN32GO&Vj/SgMXH>Lg)rIiS\]\7M-Tm`[1\7[IFca:?##B *9U:/ZK"9XZmM869Sg8mk( stream 693.3 563.1 249.6 458.6 249.6 458.6 249.6 249.6 458.6 510.9 406.4 510.9 406.4 275.8 X]:ASKd0;#"[fJ0k,p'?H0/#6P/0jL?2'%QCdXdk_+:1QaA'k9Sr#;?ZD>]!qtbJ<0Q? (>) ISBN 978-0123814791. [;JBcWFE8R&ATMF'G%G2:+:TVC+@g-^C`lYbDK?q7F!+m6Eb/ltF*(u6ARlp*Ea ;7W63Y3h>%K4j2BA1-h#4F&r[R[f<93Qir0-DbQ#`"e;n.BcnOSlfi\;ESMRk=*+d1GdPT]^deR]b&f6]+C#u7o3\_JU`LGAs)mg?5"Qp+`;OcH All righ ts reserv ed. Data Mining: Concepts, Models, Methods, and Algorithms,. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. Q-7^n5N-* 01/09/2019; 13 minutes to read; In this article. In the process of data mining, large data sets are first sorted, then patterns are identified and relationships are established to perform data analysis and solve problems. endobj << /Filter[/FlateDecode] �������P�20�06�+ U2; �9 fG�&��..0[A�@�,.] ;7ifn6:)#,k/CLP@/\8f\]gVJW/",ah`7B#=#`3^4rep/UNK'G9UH@O5)#l30W'r8+GX 6j$fNAA]? What are you looking for? n5Tk^$/QbY9;?hCr_4,m!r1(kGuVt4TS6EmY&n&^LNOTuBa6)YX//"9!s>W)a%5&o**8 /FontDescriptor 40 0 R P%qF>HAlShT?1RM4%D%e$$ihM^W\5aoTbsnnE]TfS9_XAX[dUa*K=.i:1lkCk:DW9>B; 43 0 obj 0S]eLNh6uF2BeircqW Data mining : concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. N!�n� Tìm kiếm data mining concepts and techniques 3rd edition solution manual pdf , data mining concepts and techniques 3rd edition solution manual pdf tại 123doc - … T&$VEc>r;@'k/g*GrC`lViB4uC.+AuclATDs*A1gAdCht4m@;^-/BlbD*+ED%+BleB pGo%.tXBe0.-D$+(s(b0nhP\k(p0H!Sppj"_+kVh)E^`6jr8rNh[(5.5!^KM\80cF>0* VlP7O@l21!3QXV8n`^9E2G_2V'brCr"=C^4Y'0rVP^/#BSOA;qJ"];iAR8l3,/VNI!Mf 489.6 489.6 489.6 489.6 489.6 489.6 489.6 489.6 489.6 489.6 272 272 272 761.6 462.4 V.m8!Q"(!_'g[rGfUGA �+8��8����!Q�R�ܷ/�I�di�"V�eq��Δ4� /Name/F5 FKlU'mH%e"Q3V+9AN'kuHl:WYb8jf67ct#.Qri\bY!bZ$FWCc"nR1Y1_sOW]X)+2h&d& m*)H*-!Z1ugZL'Nj:,Z#0fjI(kK=p'L*5r]l0kf$.`6IU>HZkTibRil01+[BJ1\rht7? (,^aei[$fg90N >> Data Mining In this intoductory chapter we begin with the essence of data mining and a dis- ... derstanding some important data-mining concepts. BgD2Z^L_X9)T0\0n+:n;S2\oo:,W-ghnFTc,ZU]jj! | Find, read and cite all the research you need on ResearchGate What types of relation… 0000001495 00000 n 319.4 958.3 638.9 575 638.9 606.9 473.6 453.6 447.2 638.9 606.9 830.6 606.9 606.9 << ISBN 1-55860-489-8. /FontDescriptor 11 0 R [ ]V5]6Hq2,AS.PTerr5T/AV(c!5\pO$N The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. 2p@(^j:2RQCs^[Va+%Z/eSul`pWd,cVOfO5]tC(CsQ?kuHT(9%UV%__5V1r,1h:47']_ oSDHak9.jhqRU4lA,>upI;hrEKuaIDo*4emUU>0pPG4(W&mZ=QL(P,,;uen0prRCo[k(YG-4GFhq:L]J[E$>%.I"K6*DikKf%(rKUEt/dJtf,iX%AM_YR=(8S@dA:8 +:mnVNnk!W.H]W0JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG1 306.7 511.1 511.1 511.1 511.1 511.1 511.1 511.1 511.1 511.1 511.1 511.1 306.7 306.7 (f) Predicting the future stock price of a company using historical records. p8&0aiP? /F2 12 0 R J?!o*o@LpEY=KF3@cNG"/Z?Zg3*HCr0? Data Mining: Concepts and Techniques 2nd Edition Solution Manual Jiawei Han and Micheline Kamber The University of Illinois at Urbana-Champaign °c Morgan Kaufmann, 2006 Note: For … e8f-.UURoW2*'n4sSD;nA08HjgYMneQ*'.8f54sfa460n7ie"B.QR@r9E[5+n,rB)]ol ('RKNSM]!RF(t[:tR>Z&5ptVj%g7]BQCL\F0pKT;D`)Y#]1N/Id#9;D+UkgjHW)D3 Jiawei Han, Micheline Kamber and Jian Pei. PDF | On Jan 1, 2002, Petra Perner published Data Mining - Concepts and Techniques. Chapter 4. GI=mbt_*BNSf0VN:u?YNdjQ"HCCPP%U#+lVV+3h_ :`/E3UeCS4ij_1D\EWM9jF*\k2a\?Q@kM]3Caa1YR>PPIe 0000000631 00000 n Review Questions and Problems. R<17]hC:FdE@&F&Q8$Ln-i^dkUq5\P$PSo^-puBKkeA=`MHiQ!SOjdgOUUo$MSg@oOth A2(=ZRVl4^HW-cAeZ^8J?phF_fb*VQ1cC^Q!QEeNM8'lt;%"N3LJDTn. (j(ibcr!g4eoD!^Pi#qE_=rCPjRc8h)gKmQGm0fE?e%+_!G0nCZD<79$EHhPdReJ;$rM �fz& 6/0XIm;%,1[RW-$X/80X/=%hb-"Z&X/Km0D]#1n$k&['pt,lHLhL@)!oTP.uT%ehLL<5 S[\3WdBWrLtUh?6jbd#[8hgn5o/_jcDDiQHd[=9q`#S*qJcbI% Business Aspects of Data Mining: Why a Data‐Mining Project Fails. /F6 34 0 R �����D0�@ nn8Bbr'?p_WnNo>/?X1"WPANg&-gtZO9J9R)BEY#*AW)RdVR;P;Pk-j[Z]*7e`LU1%go Le terme de Data Mining est un terme anglo-saxon qui peut être traduit par « exploration de données » ou « extraction de connaissances à partir de données ». %[eU14F/7hHs5_E0p"0jGE\dEjk,$0>HjE%4r'6]#WrnpZP5]agk(cXiKfZV=$gC-06F G170JG170JG170JG170JG0i0JG170JG170JG170JG170JG170JG170JG170JG170JG17 Data mining refers to the process or method that extracts or \mines" interesting knowledge or patterns from large amounts of data. >> Data Mining Concepts. %=%7GA@4_(!r\Bj%C#2%>/^@UeKh^IQ[(`;Ais1)L^L*H0Cb-4>,LatU@S"T:R2"e*W'Z /FontDescriptor 47 0 R DATA MINING AND ANALYSIS The fundamental algorithms in data mining and analysis form the basis for theemerging field ofdata science, which includesautomated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and … =KT++@C'dH#dV3BQP@O3B0#M+>Gl:0Ha>.AKX]cDJsWBE+L.ZDfTf8Eaa'(Df-[oCh#( These queries can be fired on the data warehouse. 0000000764 00000 n /Font 43 0 R nZs_)ROp`caQ"]QP7FK>K&O"XFCjUsM"*NN(deZ`XKQrgKg ��x�"��7�+���"9a����y� D�Û0�pC#���0JIᗫ��8j�)�w�3�8�^eFzI�uWw���I���Ǔ���ƯG���xd���}���rvp���jY��o��^�/�)O�. 9LT2=rn@D0Ri;d-qQZj@7;)[C*iM*R'"g.mWWQ2r]>H4!E:1"RG`X:Hpn`SF#\U]C#q` Data mining is deprecated in SQL Server Analysis Services 2017. H?A&O2NV7%n31Zo'1ct\kjWs8g5kTj^U,C/rP)EhTd)2529^1]?m'M)(LpfU!A,,gQ!h 818oh*NO@%8Q/"i,Fq\Pg=;n];nYL17%Z/>s?^hC?i"5-(;$:cOl_Yd? H11V=Z-$\*?a0`$<5\*\H"H#bZfF8^XR/d@g1J=JG0S*$th-5@+H6llq(=@]ph([oH+7 /LastChar 196 Concepts and Techniques, 3rd Edition.pdf. endstream endobj 182 0 obj << /Type /Font /Subtype /Type1 /Encoding 180 0 R /FirstChar 0 /LastChar 255 /Widths [ 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 271 479 584 542 719 750 271 385 385 469 584 271 333 271 281 552 552 552 552 552 552 552 552 552 552 271 271 584 584 584 375 979 781 698 771 802 635 604 813 833 333 333 719 615 885 844 875 656 875 677 604 719 823 719 1167 813 708 698 438 281 438 584 500 271 531 583 500 583 552 302 542 583 271 271 552 271 958 583 594 583 583 448 427 406 583 510 781 552 510 521 385 281 385 584 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 271 500 521 219 552 552 521 552 240 563 594 323 323 594 594 278 500 510 510 271 278 552 354 271 563 563 594 1000 1083 278 375 278 333 333 333 333 333 333 333 333 278 333 333 278 333 333 333 1000 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 1073 278 354 278 278 278 278 615 875 1094 396 278 278 278 278 278 781 278 278 278 271 278 278 271 594 875 604 278 278 278 278 ] /BaseFont 179 0 R /FontDescriptor 183 0 R >> endobj 183 0 obj << /Type /FontDescriptor /Ascent 701 /Descent 234 /CapHeight 701 /XHeight 461 /ItalicAngle 0 /StemV 145 /StemH 95 /FontName 179 0 R /FontBBox [ -177 -250 1167 929 ] /Flags 6 /FontFile 181 0 R >> endobj 1 0 obj << /Type /Page /Contents 3 0 R /Resources 2 0 R /Parent 167 0 R /MediaBox [ 0 0 595.28 841.89 ] /CropBox [ 0 0 595.28 841.89 ] /Rotate 0 >> endobj 2 0 obj << /ProcSet [ /PDF /ImageB ] >> endobj 3 0 obj << /Filter /FlateDecode /Length 4 0 R >> stream f&$'JMoq7lr"gCDS4eu6"RJ%&&'jf Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Solution Manual of Data Mining Concepts And Techniques 3rd.Ed.--Jiawei Han, Micheline Kamber and Jian Pei.pdf iC+*CH! )B8p7oG^S8:(6FK'4Isq endobj /Font 29 0 R ^6U;okIn7^4eEN/V%gLR&.982! Data‐Mining Process. 687.5 312.5 581 312.5 562.5 312.5 312.5 546.9 625 500 625 513.3 343.8 562.5 625 312.5 @%]6N4_l(:,\?2*f[k2Q9!$\/Zq3Kp`]%1A4hRa3juJ9dNGVQ06s@%j5Ne%:PT>rd13R%cGA'[,uub%q;RS9sOdG:*Xc @R\\jf0,f&:-fC+(:0aGr$J4fuKdnblgpA`FUEdR8E* Data Mining: Concepts and T ec hniques Jia w ei Han and Mic heline Kam ber Simon F raser Univ ersit y Note: This man uscript is based on a forthcoming b o ok b y Jia w ei Han and Mic heline Kam b er, c 2000 (c) Morgan Kaufmann Publishers. >> This book is referred as the knowledge discovery from data (KDD). (2) Symmetry: d(x,y) = d(y,x) ≥0 — the distance between any two points is the same in both directions, and is non-negative. V$?;C7@$R2@5?sa'>e!8?Eh:O)=#9YQ[sBdJ:O1ine7M!bQ!'+Xep_Gj/XP3l>]6O="aEVNYGN:EX3"!' -1\p\/Abj>6158!`-Y=(Toh%1\^@h\Aj3Fe]-Re%R(sWJ-MV(6n=_4iXIHH-2X_86ug1 f)u$rpMUl%s#uE=b]2CZbXYT2fajP%>tcCjZV!]lmE8Fs! Cb$'gjLJlM7-Sq6O%p`?r#%TF;m@k`!lC&hp)Q;H$oEr4#9q,UrSBb-41FpR@rIbn+9> ^t>o+O]c6]i#LIo$-S/IE$K9J^`;S+&%aa?QU66FoJ:mOrUVJ16aH$T$$*-6Q//d^l7V << 5 1.3 Data Mining—On What Kind of Data? On the same note, Mostafa [4] took a review of some techniques and applications of data mining concepts. /Widths[1000 500 500 1000 1000 1000 777.8 1000 1000 611.1 611.1 1000 1000 1000 777.8 ISBN 978-0-12-381479-1 1. 444.4 611.1 777.8 777.8 777.8 777.8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5*;ZMbK&L%4*;)>4,:(0//BhlZ]u,kIQRtnn$g/3;pbgU39dg#WNU/E1'qKj:/ /BaseFont/GZWBQY+CMSL10 b*b>1:H!#SAL-irhPX:W]nhk*C3aL%,6>%E+07-;kB0HX;j/-'=9ONM2S_Cnb7U/BL\Z %�Q��_�w[;Y�.����BeQM�0�}?��\kGó���+$^lSC���&���Q����W���+T5�Ԉ&Lt��hA�j���vQ���֙#�&�(6�\��I++�B\��a�����/�`����s���l��O��G1�L�$�*DR����-��R5*(��+���KS�,�����;���� s�Y�}�Y��=Q�! ck2/gj-nkoTH0TEj\H\a)n)N?J,@BT9C8Yo! !+*5u=F(#YN.GeRUES0H%G*RE: H�T�Kk�0�����C4���uz3�zi�f$^K;vؗ�B�<0ƒ���/�� '��}u�.uۜ�83R���߿� �ey�����N�"���4 8l���_곡�厞=��k���+T�� gI)kOp&cu7u1M_"$Vs`6^>VpsR)tIVeqp0gCPQ("Y+3Z!N%V+eGkKLd^!>@oU^jA0 stream ^O?1G5K\eWB^n]ma%*UYg:c.\(#&:dc,eS>hRg3bhM>%qQ=K?bDbpUC9G5\h/cebiYuJ H�lTMS�0��W�3��B���� �����C�I �L��+K�-��8�����v�e@��@s�iN�f����la^��HqAx��Z��^D�崃��`�BQ8�`&@ �O�9���?�y�jH$'�",�)K��`kD�\��Y��А0D0�B���FQMd�a�Dd}����p].eDY��*(��ޑ��3{����a�) �Y<1��_��SK�D�p�3(���᪞U�����������k��Q�-%80Iri%���fD&O�j�j�'�1���0Mrai��O�&� �Ԕ��O�W�>)&HMR��l0рmx��6��/nM�l^-L]9g��z������f�GpS��M���f���W�Ύ�se��l��y,�29����Ҽ4�F�"z���Q��I�Xi3�ں$)U�����/)W�_T��x\����W&[��A�2O��:S83r�ڙ�3=�4>p��]|�x�sK�}P$w�9 4Ɓ�_, Q_]F;,^lr6/8^K&F "#e\%Z,J?o41u9RV[RGoEq-aS#Z%_0M)7.7T0Vr /LastChar 196 Chapter 2. G9H$O1]6EdjLh[n,U#I`GQ6/4fa]'dO40UQF5ioI4_/O:6.K]3s-"6M)FX 4M6Zbe7HB^L>2*gE@?q"C>[U%7=;rV;o! Download full-text PDF… 0000003846 00000 n A model uses an algorithm to act on a set of data. BkA]Wu`jqBLE0*rdL52lqm%ldQ.=oX)$SfT`i\BN$g'S>m1fJ9T1:lU8pFTAe!Su>kPd ��CW"t�Ќ�����5*� B�%����j;1F:T~�p8��@����@+���@������O(�uu�V�t>�6��ǧ�.l����`�!����;և��:�U�FX k�C}����p���k��N�R3A��R�H%I*m�q�RN�ͭ'j������/� �u� endstream endobj 178 0 obj 292 endobj 179 0 obj /GillSans-Bold endobj 180 0 obj << /Type /Encoding /Differences [ 32 /space 33 /exclam 34 /quotedbl 35 /numbersign 36 /dollar 37 /percent 38 /ampersand 39 /quoteright 40 /parenleft 41 /parenright 42 /asterisk 43 /plus 44 /comma 45 /hyphen 46 /period 47 /slash 48 /zero 49 /one 50 /two 51 /three 52 /four 53 /five 54 /six 55 /seven 56 /eight 57 /nine 58 /colon 59 /semicolon 60 /less 61 /equal 62 /greater 63 /question 64 /at 65 /A 66 /B 67 /C 68 /D 69 /E 70 /F 71 /G 72 /H 73 /I 74 /J 75 /K 76 /L 77 /M 78 /N 79 /O 80 /P 81 /Q 82 /R 83 /S 84 /T 85 /U 86 /V 87 /W 88 /X 89 /Y 90 /Z 91 /bracketleft 92 /backslash 93 /bracketright 94 /asciicircum 95 /underscore 96 /quoteleft 97 /a 98 /b 99 /c 100 /d 101 /e 102 /f 103 /g 104 /h 105 /i 106 /j 107 /k 108 /l 109 /m 110 /n 111 /o 112 /p 113 /q 114 /r 115 /s 116 /t 117 /u 118 /v 119 /w 120 /x 121 /y 122 /z 123 /braceleft 124 /bar 125 /braceright 126 /asciitilde 161 /exclamdown 162 /cent 163 /sterling 164 /fraction 165 /yen 166 /florin 167 /section 168 /currency 169 /quotesingle 170 /quotedblleft 171 /guillemotleft 172 /guilsinglleft 173 /guilsinglright 174 /fi 175 /fl 177 /endash 178 /dagger 179 /daggerdbl 180 /periodcentered 182 /paragraph 183 /bullet 184 /quotesinglbase 185 /quotedblbase 186 /quotedblright 187 /guillemotright 188 /ellipsis 189 /perthousand 191 /questiondown 193 /grave 194 /acute 195 /circumflex 196 /tilde 197 /macron 198 /breve 199 /dotaccent 200 /dieresis 202 /ring 203 /cedilla 205 /hungarumlaut 206 /ogonek 207 /caron 208 /emdash 225 /AE 227 /ordfeminine 232 /Lslash 233 /Oslash 234 /OE 235 /ordmasculine 241 /ae 245 /dotlessi 248 /lslash 249 /oslash 250 /oe 251 /germandbls ] >> endobj 181 0 obj << /Length1 1057 /Length2 24629 /Length3 532 /Length 33257 /Filter /ASCII85Decode >> stream @VHPdhVG=l!j7':H:=dOqfOZ%I(u>o>]&]O%odqo\ (R`CVqSSf&,)>iS3\_4Z[*[_Cl"@Ys-`PHP#Y>3dY"JLAjRHbEKG\s[rGnn/2^# endobj Some of the exercises in Data Mining: Concepts and Techniques are themselves good research topics that may lead to future Master or Ph.D. theses. )ra)pB9DO\/'BlJ1Ae3*sik893_J14I(r^"h/?oJ$aeQ07Df&$5Ot=?H#BoOo\+BrqLo /Type/Font 70JG170JG170JG170JG170JEqi0JG170JG170JG170JG170JG170JG170JG170JG170J �fz& if>GPY8OJbQ]U.hXd<2*pj>=08Z(@VusNm>]k/q]:;X]t!eJ5bfU,mk$ImL+IqS;%lGi Large Data Sets. **A]sPIn9pX.EHiMJ%r&8m$5Ln#sg?M0bJ*$`-co3(C1:2-YL,;+T%@L7Z2`UnBk8ASl /BaseFont/MGZMVE+CMSY10 :l[R]-d#T^WO3 stream Tu@KE2W"(MRMN8&>=On_6.d*"JS,gHf['9YS3*:/(-=*]"bXEYlD?>RHcJRID._qk*Q& 0&UPEbIL/I?4tJS;G2o^E,sg&>o*=0efVbENI5/WCot!/Ci5K!NC;sWE9ZsQjqj%nsDW*LG4u+[OQ@%[d$1#4W2iLeRR:Ab%kjO> A discussion of advanced methods of clustering is reserved for Chapter 11. << Itq,iATc5RU6tOW?G-*MWf2$=+=rfcBW+/D!X4Mh##!hUhm09KQ!i]1-?pTt/;2&G#k1&ph5_!&e1-E$[ipU\h:cH1r9T5rIT[&=\[k06dBG*9'Y65^g,!>s:R.e!YNM'"5,C=_$QA8qsL?hq \SBfN]Mul!c0PO\`mMp.RinD^PU52m3:Uot[ *m'08^E'C`9jN/Cne0a^Ed!.dE7D%"&W8@/eh!8g+`E\/]NDh"nu4% ^(*dnp+Hg+K0ktSS`L[jbK#el:/&[$()Zuaq;!`Gm0^$3,GH$1ih%4Q0sdfFB>2*`q(/ /FirstChar 33 g?o!5AO:MI%F"S[Wc]PHGQc[D*tc)c>R/A]&J*eht0/7aj!bQOTN8& dp$lG=9GNiK;5l#f/o Avoiding False Discoveries: A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results, which is novel among other contemporary textbooks on data mining. endobj /Subtype/Type1 "9q2J?bP[V_@;t8_I:$=?NtM5M.,ihUJDF Introduction . +Co%q%511hDKJW^D.OhC7qldU;djQb/OFAlA0>DoAdpC^DI[TqBl7Q+;flGcA79Lh7;c G170JG170JG170JG170JG0i0JG170JG170JG170JG170JG170JG170JG170JG170JG17 Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Ck^F_50^]E>351FU'Q:YO%#V5PEJoUo>4j)nO#RtNpb%0@eNk!tX%Gi,7HPA6jdE!$&X##^E3=gE`'",?NIEMIfb/59PKEd)?j"JDuR'h=,ebN:b(h7q[ahUHq5.Gn QA76.9.D343H36 2011 006.3 12–dc22 2011010635 BritishLibraryCataloguing-in-PublicationData A catalogue record for this book is available from the British Library. URA8,po+Co%q%51OdBl8#lH#R=;0Han;AdpC_Df0YrH#R=;0d("=ol+>>E%0JO\(+>@(DEb/ZiDf0B:+Co%q%51_!BlS9,8OO[W2DdBK+Co% /Subtype/Type1 15 0 obj << a:#:rChnC@H%s*Z"/. 500 500 611.1 500 277.8 833.3 750 833.3 416.7 666.7 666.7 777.8 777.8 444.4 444.4 593.8 500 562.5 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 NJS9U'H/nO9"+b-7$]5LK/.k',)EX-C@igbI6nH9)H.!0.PC#\^)T08tdF,\WS=&=YB,&6A@rfGK/[mI]@?-V7 /FirstChar 33 �F�z Data mining: concepts and techniques by Jiawei Han and Micheline Kamber. �z�f << 70JG170JEqi0JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170J /Length 845 >> Wr;k1=lgF9kZrkkgnn!^k\7LgBjPK#m>"D'0jiO=*1&Vo*]7@c'd,HeWS][Z1aYN%#>= Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. 511.1 575 1150 575 575 575 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 q%511hDKJ33Dg6U\0fM$6/M]1;+>GT/2]t+93IPpZ@:X:oCj@.6AS)B]F`MM6DKKE'@r Na(4(!X(V(8qe'$^IR1('iG'"C0oAO,;\H/ag5RVh1W? This is our adopted textbook, from which this set of lecture notes are derived primarily. qWjC))nr>P-L^cS@nc]YQ;B'C[! �g?&�C��C*����ǻ���F�UW-j�i�e�Z*+W]�����UsA^��&v��7c��@�m]��>WK9G���4$�l.+k��4"iݐ: i�.��7�:�J�z���M��Є�0n���i�0����N1��P����oM��Z��$:���O ===A�O;�u[��>V�Mq�׾.�I��f���A�B�6��x�P.KnX0s;�� w&Eh�A��ܸ���! x�S0�30PHW S� 41 0 obj @>n$tAeNp$\?o>1YRh"V="ULX] 500 555.6 527.8 391.7 394.4 388.9 555.6 527.8 722.2 527.8 527.8 444.4 500 1000 500 /Type/Font Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc. /BaseFont/JYEXKW+CMR17 >> 5-ONRY:obIXE:JH^A\>Jdp/B]T>^tB]GkuC!T^K(`Js 42 0 obj K#[P>\aIk'S]p\7EQV*gS+[ggf"VrD(P))4Z2iu@3Ekgs=WjX3'OIdTM^t(nc^lA@jnZ YqFe l1^uhu!lPgF\s%na"D'@^A Itq,iATc5RU6tOW?G-*MWf2$=+=rfcBW+/D!X4Mh##!hUhm09KQ!i]1-?pTt/;2&G#k1&ph5_!&e1-E$[ipU\h:cH1r9T5rIT[&=\[k06dBG*9'Y65^g,!>s:R.e!YNM'"5,C=_$QA8qsL?hq 170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170JG170F]]i0 H�T���0Ew�]c�����S(j+?��6m�(J�sr,hY�"ņs�v������OS��m�Z��R�'5�)���]0�Ƒ:�34[|��I�כw��e/z�.`(�D=>B�&qX�9�ߥ5�e�0�"�—<3A'�qc�M�r�OL��ۉ�BmZ����2UҞ���R1U�2Kސ� ��T endstream endobj 4 0 obj 194 endobj 5 0 obj << /Type /Page /Contents 7 0 R /Resources 6 0 R /Parent 167 0 R /MediaBox [ 0 0 595.28 841.89 ] /CropBox [ 0 0 595.28 841.89 ] /Rotate 0 >> endobj 6 0 obj << /ProcSet [ /PDF /Text /ImageB ] /Font << /F34 118 0 R /F33 117 0 R /F31 116 0 R /F32 182 0 R /F30 182 0 R >> /XObject << /Im1 9 0 R /Im0 10 0 R >> >> endobj 7 0 obj << /Filter /FlateDecode /Length 8 0 R >> stream ,p?)`/OGZ51G_'G2]sh83ACsh-! 2012- Data Mining. -Eb/c(FE1f#ASkjrCERP-+B3#c+AHclDfg8DAKX?YEc>r;@4%DBMbl@kK2QEb/ZiDf Important. /Subtype/Type1 O/AbE*J7*Lp$>$.LDK_"Xl&W4*8\#i&n0( It provides several hands-on problems that need to practice and tests the subjects taught in this online book. /Type/Font H�b```a``�"03 �0+P�#@�>*�}�[сi�� II. Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzic PDF, ePub eBook D0wnl0ad This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. endobj These include the TF.IDF measure of word importance, behavior of hash functions and indexes, and iden … k`>.lQh,h38NPO endobj The main parts of the book include exploratory data analysis, frequent pattern mining, clustering, and classification. @T@?T-9Rn[RktGeY9"\"?W �@0T0V�]����4)D-��:���HQR�o���ic���m�%�c����ٟ�ɏ�1r���E�8H� Data mining (lecture 1 & 2) conecpts and techniques Saif Ullah. The notion of automatic discovery refers to the execution of data mining models. A guide through data mining concepts in a programming point of view. /Type/Font HAN 17-ch10-443-496-9780123814791 2011/6/1 3:44 Page 444 #2 444 Chapter 10 Cluster Analysis: Basic Concepts and Methods clustering methods. 777.8 777.8 1000 500 500 777.8 777.8 777.8 777.8 777.8 777.8 777.8 777.8 777.8 777.8 H>j#H3Z,_br/dAf%nn+k0m7]i09RIU$qaBBoI2VNe`D5r>6AOpq>5Pc%$q@kBe[(>W>ICE8FB,&L,X !`_F(&]m3Zp171b^d01GLXF2[q&Z+ /^h.3B&h(3X]878i./O.X&'IBr!q46Ye_>Y"V.JHaG42dUCn17!q"\)HR3XGB9Ap).Y>Q6b3Ql@KTWBCW!:\QdYU[b"qL*mNcs. << ;D)rV'l8@oiK7OX))jG`Kh,f\tMs4uD9RRMN[:*)m$TTb-7`j#^-3"Aaj]%kH&S!h2IZ VlP7O@l21!3QXV8n`^9E2G_2V'brCr"=C^4Y'0rVP^/#BSOA;qJ"];iAR8l3,/VNI!Mf /Name/F3 :%'l5d 0000002121 00000 n Oo-jk[`Z=BX)^VnKT=Yg/7$>N2"%0;>Cj\"(jjYK#Br4r'h;.f3*8Ie=d#d6%m=oPU%! if>GPY8OJbQ]U.hXd<2*pj>=08Z(@VusNm>]k/q]:;X]t!eJ5bfU,mk$ImL+IqS;%lGi Organization of This Book. f-X@TaGGW1q7WmE?aHFU?A:,%Wfn>&);[g]C-H8_Wl922fr1#2KSq?%oF#lB`A]hIco6 249.6 719.8 432.5 432.5 719.8 693.3 654.3 667.6 706.6 628.2 602.1 726.3 693.3 327.6 stream Large Data Sets. ZLLK]#@p6S0Agl=cP(,%'! :9AroesBO"5K=h5tK2%Dsh;*7+eYfMeS:Hr,G6O2:iH7&\;c%_:)4 462.4 761.6 734 693.4 707.2 747.8 666.2 639 768.3 734 353.2 503 761.2 611.8 897.2 imZO('0(,;*$[9lPkt#Sa7C<4$+O^:\_IS;lHfWQE)$OH=Y(,T%O-kZKX__$K;E$6*Qqj`=aF^]XnE`$O?9`3`;mA[2HpkrhcfF)D:)LQBWB@r\N'sQ4XJa(O?tc+l( (#DW]"KKgo:DAllitJ9bT@:D Tujuannya tidak lain adalah agar end-user dapat melakukan data mining … endobj �E�xRk{|�����c�'Y�/Z�&��&z�]!��r�d�O�^����%�y+/JII�=�|���? /FirstChar 33 endstream Documentation is not updated for deprecated features. endobj Do not copy! "r'5/AF*Q+VbGO4adXe[2eKCP[D[7`]T-Im-8Q7.HmOJ? endstream endobj 182 0 obj << /Type /Font /Subtype /Type1 /Encoding 180 0 R /FirstChar 0 /LastChar 255 /Widths [ 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 271 479 584 542 719 750 271 385 385 469 584 271 333 271 281 552 552 552 552 552 552 552 552 552 552 271 271 584 584 584 375 979 781 698 771 802 635 604 813 833 333 333 719 615 885 844 875 656 875 677 604 719 823 719 1167 813 708 698 438 281 438 584 500 271 531 583 500 583 552 302 542 583 271 271 552 271 958 583 594 583 583 448 427 406 583 510 781 552 510 521 385 281 385 584 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 271 500 521 219 552 552 521 552 240 563 594 323 323 594 594 278 500 510 510 271 278 552 354 271 563 563 594 1000 1083 278 375 278 333 333 333 333 333 333 333 333 278 333 333 278 333 333 333 1000 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 278 1073 278 354 278 278 278 278 615 875 1094 396 278 278 278 278 278 781 278 278 278 271 278 278 271 594 875 604 278 278 278 278 ] /BaseFont 179 0 R /FontDescriptor 183 0 R >> endobj 183 0 obj << /Type /FontDescriptor /Ascent 701 /Descent 234 /CapHeight 701 /XHeight 461 /ItalicAngle 0 /StemV 145 /StemH 95 /FontName 179 0 R /FontBBox [ -177 -250 1167 929 ] /Flags 6 /FontFile 181 0 R >> endobj 1 0 obj << /Type /Page /Contents 3 0 R /Resources 2 0 R /Parent 167 0 R /MediaBox [ 0 0 595.28 841.89 ] /CropBox [ 0 0 595.28 841.89 ] /Rotate 0 >> endobj 2 0 obj << /ProcSet [ /PDF /ImageB ] >> endobj 3 0 obj << /Filter /FlateDecode /Length 4 0 R >> stream