Data mining: concepts and techniques
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| Format: | Book |
| Language: | English |
| Published: |
Amsterdam [u.a.]
Elsevier MK
2012
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| Edition: | 3. ed. |
| Series: | The Morgan Kaufmann series in data management systems
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Physical Description: | XXXV, 703 S. Ill., graph. Darst. |
| ISBN: | 9780123814791 |
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| 100 | 1 | |a Han, Jiawei |d 1949- |e Verfasser |0 (DE-588)137798342 |4 aut | |
| 245 | 1 | 0 | |a Data mining |b concepts and techniques |c Jiawei Han ; Micheline Kamber ; Jian Pei |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier MK |c 2012 | |
| 300 | |a XXXV, 703 S. |b Ill., graph. Darst. | ||
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| 700 | 1 | |a Kamber, Micheline |e Verfasser |0 (DE-588)1146286937 |4 aut | |
| 700 | 1 | |a Pei, Jian |e Verfasser |0 (DE-588)1021437980 |4 aut | |
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| _version_ | 1824553804892209152 |
| adam_text | Titel: Data mining
Autor: Han, Jiawei
Jahr: 2012
Contents
Foreword xfx
Foreword to Second Edition xxi
Preface xxiii
Acknowledgments xxxi
About the Authors xxxv
Chapter I Introduction I
II Why Data Hinlng! I
I.I.I Moving toward the information Age I
I. ( .2 Data Mining as the Evolution of Information Technology 2
1.2 What It Data Mining? 5
1.3 What Kindt of Data Can Be Mined? 8
1.3.1 Database Data 9
1.3.2 DaU Warehouses 10
1.3.3 Transactional Data 13
1.3.4 Other Kmds of Data H
1.4 What Kindt of Pattern» Can Be Mined? IS
1.4.1 Class/Concept Description: Characterization and Discrimination I 5
1.4.2 M)n»rtg Frequent Patterns, Associations, and Correlations 17
1.4.3 Classification and Regression for Predictive Analysis 18
1.4.4 Ouster Anafysis 19
1.4.5 CXitlier AnaVsis 20
1.4.6 Are All Patterns Interesting? 21
15 Which Technologies Are Uted? 23
1.5.1 Statistics 23
1.5.2 Machine Learning 24
1.5.3 Database Systems and Data Warehouses 26
15.4 Information Retrieval 26
lx
Contents
t 6 Which Kindt of Application» Ar« Targeted! 27
1.6.1 Burnett Intelligence 2?
1.6.2 Web Search Engine* ÌB
1.7 Major Issue« in Data Mining 2?
(.7,1 Mm.ng Methodotogy 2*
(7,2 ihef Interaction 30
1.7.3 Eff *ncy and SeaUjMy 3)
1.7.4 Over uty of DaUbaie Typ« Ï2
1.7.5 Data Mining and Society 32
18 Summary 33
1.9 Exercises 34
1.10 Bibliographic Notes 3 S
Chapter 2 Getting to Know Your Data 39
2.1 Data Objects and Attribute Types 40
2.1. ( What 1$ «in Attribute? 40
2.1.2 Nominal Attribute-, 41
2.1.3 Ékury Attribute* 41
2.1.4 Ordirai Attribut« 42
2.1.5 Numeric Attributes 43
2.1.6 Discrete versus Continuous Attribut« 44
2.2 Basic Statistical Descriptions of Data 44
2.2.1 Measuring the Centra! Tendency. Me#v Medina and Hot*? 4S
2.2.2 Measuring the Dispersion of Data.* Punge. Qwtiïcs. Veranee.
Sundard Deviation, and Interquartife fùnge 48
2.2.3 Graphic Displays of Basic Statistical Descriptions of Data 51
2.3 Data Visualization 56
2.3.1 Pixel-Oriented Visualization Techniques $7
2.3.2 Geometric Projection Visualization Teihn»qu« 58
2.3.3 Icon-Based Visualization Techniques 60
2.3.4 Hierarchical Visualization Techniques 63
2.35 Visualizing Complex Data and Relations 64
2.4 Measuring Data Similarity and Dissimilarity 65
2.4.1 Data Matrix versus Dissimilarity Matrix 67
2.4.2 Proximity Measures for Nominal Attributes 68
2.4.3 Proximity Measures for Binary Attributes 70
2.4.4 Dissimilarity of Numeric Data; Mmkowski Distance 72
2.4.5 Proximity Measures for Ordinal Attributes 74
2.4.6 Dissimilarity for Attributes of Mixed Types 75
2.4.7 Cosine Similarity 77
2.5 Summary 79
2.6 Exercises 79
2.7 Bibliographic Notes 81
Contenu xi
3 Data Preprocessing 83
31 Data Preprocessing: An Overview 84
3,1.1 D.it.1 CM«/: Why Preprtxe» she D.iu 84
3.1-2 Mijor T«ict «n Dal» Pnfproceiurtg 8S
32 Data Cleaning 88
32 1 MnyngVjlu« 88
32 2 NonyD.1« 89
32 3 O.H.Ï Ocwvng » 4 Process 91
3 3 Data Integration 93
3 3.1 in i y Identifie,i1»©n Probtern 94
3 32 Rcour«dar y and Correlation A/uty« 94
3 3 3 T(?pîc Duf 1 ,u»on 98
3 34 O,ita V*luc Conii.« Dtterton ând Résout;«« 99
34 Data Reduction 9°
3*1,1 Gwrrvicw of Dati Reduftion Straicg« 99
3-Q W^-ckn Traniíormi 100
3.4.3 PrwKipâJ Cctfr poreftU Ana.yv*i 02
3, ».4 Atinbute Subset Selection (03
5.4S Rcgnrivon and Log-Linear Modefe Parämeinc
Dati Reduction 105
3.46 H(S10gr,ims 106
3,4.7 Clustering 108
348 Sampling 108
3.4.9 Data Cube Aggregation HÖ
35 Data Transformation and Data Discretization 111
3.5.1 Data TramfCKTTsatton Strateg« CK^n^rw 112
3.5.2 Data Transformator* by Normaiaation 113
35.3 DtstretoUon by Bmning 115
3.5.4 Discrcteitlon by Histogram Aruîy« 115
355 Discretization by Gusten Dec*oo Tree, and Conndation
Analyses 116
35.6 Concept Hierarchy GeneratKXi for Nominal Data 117
3.6 Summary 120
3.7 Exercises 121
3.8 Bibliographic Notes 123
Chapter A Data Warehousing and Online Analytical Processing I2S
4.1 Data Warehouse: Basic Concepts 125
4.1.1 What is a Data Warehouse? 126
4.1.2 Diffenmces between Operational Database Systems
and Data Warehouses 128
4.1.3 But Why Have a Separate Data Warehouse? 129
xlt Cmtenti
4.1.4 DaU Wjrehoywf A Mgftftwd A«bti«t!urts 130
4.. Ì .5 Data Wjrehovte Hcxtch. C««rp^ne W*r^o«jsr. O-tiu M* î.
and Vtrtu.il W«rNJ%Ae 13 2
4 1.6 Extraction twsforrraton ar l leming H
4.1.7 MeUdau Repewtory i 34
4 2 Data Warehouse Modeling: Data Cub« and OLAF IJS
4,2, t Qjta Cubr. A Huhid rrcn-,iQftal Díí,* Ho»:*?* 13*
4.2.2 Stars. SnowfWc. *nd Fací Con-,tpf!.«!iom SíNrm«
for Piuitidimeniiôfuf D.tti Modih I J*
4.2.3 Dtmcmiom: The Raie oíConcí-pí HifTj/th« 142
4.2.4 Measurw: Their Catpsaoniaìiort jwyj Cofr-puW-or» 144
4.2.5 TypiCAt OLAP Opec*î:on-, Hé
4.2.6 A Surrtet Query Mode? far Qucry^ Muft.ärrirfnioM
Datate« 149
4.3 Data Warehouse Design and Utag« I SO
4.3.1 A Burines*» Arufyvs FromcAork fer D4I4 Wjitfia« Dp--¡sn ISO
4.3.2 Dita Warehouse Oevgn Pro«-» , IS S
4.3.3 Data Warehouse Uwgç for în?ofrn,t!ion Pnxc-.v^vt S3
4.3.4 From Gnitne Arwkfyttcal ProccAfig to t K-Jorncrr¿ari4¡
Data Mmmg 15S
4.4 Data Warehouse Implementation IS6
4.4.1 Efficient Data Cube Comptjt«tt!üt An CKrrvew I %f
4.4.2 Indexing OLAP Dit* Bitmap Index and fon kxie»! 160
4.4.3 Efficient Processing of OLAP Quenes Í 6 Ï
4.4.4 OLAP Server Architecture!: RÖLAP versus MOLAP
versus HOLAP 164
45 Data Generalization by Attribute-Oriented Induction 166
4.5.1 Attribute-Oriented Induction for Data CharactenArtian 167
4.5.2 Efficient Implementation of Attrtbutc-Oncnted inductson 172
4.5.3 Attribute-Oriented Induction for Class Comparions 175
4.6 Summary 178
4.7 Exercises 180
4.8 Bibliographic Notes 184
Chapter 5 Data Cube Technology 187
5. i Data Cube Computation: Preliminary Concepts 188
5.1.1 Cube Materialization; Full Cube, Iceberg Cube. Closed Cube,
and Cube Shell 188
5.1.2 General Strategies for Data Cube Computation 192
5.2 Data Cube Computation Methods 194
5,2.1 Mutttway Array Aggregation for Full Cube Computation 195
Ccntcnn xlii
5 2.2 BUC Computing iceberg Cwbes from !he Apex Cubo«J
Downward 2O0
S .2.3 St^Cutxftf Computing Iceberg Cube* Uwr*j» « Dynamic
Sur-Tr« Structun; 204
5.2.4 Prccompunng Shell Fragments far Fa« HigtvDfmen onal OLAP 2i0
S 3 Processing Advanced Kind* of Queries by Exploring Cube
Technology 218
5.3.1 S.vrsp!«g Cub«: OLAP ßtoed Mn»j on Ssmpimg DaU 218
5 3 2 Ranking Cuber. E*f c»ent ComptAtton of Top-* Queries 22S
54 Multldlmentional Data Anafytii In Cube Space 227
5 -i. I flr*d tion C âx%: Prcáiatm Mmtng « Cube Sp*ce 227
5.4.2 Mutiifciturt Cuber Con^ krx Â|^rfgaî»on at Multate
GnvxAvrt»« 230
54-3 E«cptJ ^-B.iied Dt%ci»rtfy-Dnvcn Cube Spice Expíoration 23I
55 Summary 234
5 6 Exercises 235
57 Bibliographic Notes 240
Chapter 6 Mining Frequent Patterns, Associations, and Correlations: Basic
Concepts and Methods 243
fel Basic Concepts 243
..1,1 Market Basket Amîys.« A Motivating Example 244
6.1.2 fnsqucnt Items««. Oosed Hemsets, and As«x*at»on Flu!« 246
62 Frequent Item»« Mining Methods 248
6.2.1 Apnon Algorithm: Finding Frequent lîem eU by Confined
Candidate Generation 248
6 2.2 Generating Association Rules from Frequent Kemsets 254
6.2.3 Improving the Efficiency of Aprron 254
6.2v4 A Pattern-Growth Approach for Mining frequent hemsets 257
6.2.5 Mming Frequent Itemsets Uwng Vertical Data Format 259
6.2.6 Mining Closed and Max Patterns 262
63 Which Patterns Are Interesting?—Pattern Evaluation
Methods 264
6.3.1 Strong Rules Are Not Necessaníy Interesting 264
6.3.2 From Association Analysis to Correlation Analysts 265
6.3.3 A Comparison of Pattern Evaluation Measures 267
6.4 Summary 271
6.5 Exercises 273
66 Bibliographic Notes 276
xt* Centena
Chapter 7 Advanced Pattern Mining 37t
7,1 Pattern Mining; A Road Map 27f
72 Pattern Mining in Multilevel. Mo«tWim«mkmal Space 213
7,2. t Mining Muil.krvd A-.-ux .j!,.h*-, 2¦* Î
7.2.4 Mirtina Rarr Pat rrm Jtr-.if¿»£4;v Ptf - . 2 1
7.3 Constraint-Based Frequent Pattern Mining 294
7.3.1 Mctjrule-Gjidfd H rir¿ of A ,^- ::a .o« Putm i ^ j
7.3.2 Con*,îr4tnf-B.i*.ÇTJ F*c*îîrrrt Cfr ^ f fV^rirg ^f rrn S
7A Mining High-Olmentional Data and Co(mvil Patterm 301
7.4.1 Mmmg Coto-,i4l Píítrms by PAV.cm-fvnoft ÌÙÌ.
75 Mining Compressed or Approximate Pattern« 307
7,5.1 Mining Compre-»»«! fattemi bv; fViïtrm Oj-.tcnrg }09
75.2 Extracting Redundancy Aware Top--« Psiutrrm Î10
7.6 Pattern Exploration and Application 313
7.6.1 ScrmntK Annotation of Frequent Patterns ìiì
7.6.2 AppitCationj of P,ittern M»mng J17
7.7 Summary 31*
7.8 Exercises 321
7.9 Bibliographic Notes 323
Chapter 3 Classification: Basic Concepts 327
8.1 Basic Concepts 327
8.1.1 What 1$ C!assific«rt!on» 327
8.1.2 General Approach to Classification 328
8.2 Occisión Tree Induction 330
8.2.1 Decision Tree Induction 332
8.2.2 Attribute Setection Measures 336
8.2.3 Tree Pruning 344
8.2.4 ScaiaWfty and Dechton Tree Induction 347
8.2.5 Visual Mining for Decision Tree Induction 348
8.3 Bayes Classification Methods 3S0
8.3.1 Bayes Theorem 350
8.3.2 Naive Bayesian Classification 351
8.4 Rule-Based Classification 35S
8.4.1 Using IF-THEN Rules for Classification 355
8.4.2 Rule Extraction from a Decision Tree 357
8.4.3 Rufe Induction Using a Sequential Covering Afgonthm 359
Centena xv
85 Model Evaluation and Selection 344
8.S. I Meines x Evaluating Gtwfor farfomunce 364
85 2 Hddouî M«hod and Rrxiom Sut»*mpiing 370
85 3 CroivV4λd« cw 370
854 Beontrap 371
855 Mode! Selection Uyng Sutnixjl Tern of S gnifcwxe 372
856 Companng CUiyfa-s B*i*d on Cost-Benefit and ROC Curves 373
86 Techniques to Improve Classification Accuracy 377
861 Introduce Enjembte Mclhodi 378
862 B.w g 379
861 Booilmg and Adißoort 360
86.4 R-imtom F ©revu 382
8.6.5 Improving CUu/mbon Accuracy of Oisvlmbalaftced Dita 383
87 Summary 3BS
88 Exercise« 386
89 Bibliographic Notes 389
Classification: Advanced Methods 393
9.1 Bayeslan Belief Networks 393
..1,1 Concepu and Mechanisms 394
9.1.2 Tr*mng Bayçyan Bclrcf Nctvwxks 396
9.2 Classification by Backpropafation 398
9.2.1 AMulîlla ö•Fecd•fol^vâná^4eMf¦a!Neîvw*: 398
9.2.2 Defining a Network Tc^otogy 4O0
9.2.3 âtkpropagat»on 400
9.2.4 ln$»de the Back Box: BackprppagJlKsn and intetpretaWfty 406
9.3 Support Vector Machines 408
9.3.1 The Case When the Data Are Linearly Separatste 408
9.3.2 The Case When the Data Are Linearly inseparaWe 413
9.4 Classification Using Frequent Patterns 41S
9.4.1 Associative Classification 416
9.4.2 Discriminative Frequent Pattern-Based Classification 419
95 Lazy Learners (or Learning from Your Neighbors) 422
95.1 k-Nearest-Nefghbor CJassifim 423
95.2 Case-Based Reasoning 425
9.6 Other Classification Methods 426
9.6.1 Genetic Algorithms 426
9.6.2 Rough Set Approach 427
9.6.3 Fuzzy Set Approaches 428
9.7 Additional Topics Regarding Classification 429
9.7.1 Multidass Classification 430
jtvl Contenti
972
9.7.3 Aetiw Ledrrwnj 4)3
9.7.4 Transfer lejmng 4}4
9,8 Summary 434
99 Exercises 431
9 !0 Bibliographic Note« 43f
Chapter 10 Cluster Analyst«: Bask Concepts and Methods 441
101 Clutter Analysis 444
I0.I.I What h CKfttCf AA».y^, 444
10 I 2 Requirements (of CkrAct Anjfyv* 44$
10.1.3 Overview of « Cknîer^i H rt?Kxí-, ***S
102 Partitioning Method« 4SI
10.2.1 à-Mcim: A CentrcMtf-fkwti ictf-r^M 4SI
10.2.2 k-Mcdo«h; A Rcfment
103 Hierarchical Methods 4S7
10.3.1 AggfomcratMi vtrurt
10.3.2 DisUncc Mcasurei « Afgorrthfntc HctNxh 441
(0,3.3 BIRCH; HuftipKíSc Hermhcá tknitrmf ihthf Ch
Feature Tre« 462
10.3.4 Owneteon-
10.35 fVobabtliJW HefârcNc^ Oustcring 467
104 Density-Based Methods 471
10.4. t DBSCAN: Demrty-fcisçd CVßtcnog br,cú un
Regions with High D«n-» ty 4 71
10.4.2 OPTICS: Ordering Pbrntt to Identify the Onitwg Structure 473
10.4.3 DENCLUE: CIvRtertng B^sed on Density Dîînbjtîor» functtóm 476
105 Grid-Based Methods 479
10.5.1 STING: STatistical INforrnattw Grid 479
105.2 CLIQUE: An Apfiorrtl* Subspa« Ouîtcmg Method 481
10.6 Evaluation of Clustering 483
10.6.1 Assessing Clustering Tendertcy 484
10.6.2 Determining the Number of Clusters 486
10.6.3 Meastring Clvatenrtg Quality 487
10.7 Summary 490
10.8 Exercises 491
10.9 Bibliographic Notes 494
Chapter 11 Advanced Cluster Analysis 497
III Probabilistic Model-Based Clustering 497
I I.I.I Fuzzy Clusters 499
Centtntt xvfl
II 12 ProtMbtabc Modtf-6»«d Ouster* Ml
11.1,3 CxpcctJtiOO MissfTwaiJon Aigowivn SOS
111 Cluttering HlgtvDImensional Data SOS
I ! .2.1 Chnienng Hfgh.D m«ni©nil DiU: Probten«, OuSe«gci
am) Mijo«- Mcihodotopci S08
11.2,2 Sobîp*:e OuttcongMctNsdt SiO
112 3 B*krtlcrmg Si2
11.14 DimcnyofulJìy Redutt«x» Methods amj Spectral Gustertng S19
11 3 Clustering Graph and Network Data S22
.1,3,1 Apphcjtxxtt and Ch.»»tr g« S23
11.3.2 SmlantyMoKufCS S25
11.3.3 Graph OustenngHeihodi S28
11.-I Cluttering with Constraint* 532
11.4.1 Gategonmion oíCwwrîunts SÎ3
11.4.2 Methods for Quttenng vnth Conitrünts S3S
115 Summary 538
116 Exertltei 539
117 Bibliographic Notes 540
Chapter 12 Outlier Detection 543
12.1 Outliers and Outlier Analysis 544
111.I What Am Outten? SAA
12.1.2 Types of öut!.m S45
12.1.3 Chatteng« of Ootiter Detecté S48
12.2 Outlier Detection Methods 549
12.2.1 Supervised, Semi-Supervised, and Unsupervtsed Methods 5-49
12.2.2 Statistical Method*, Prroom!ty- a ed Methods, and
Clustenng-Based Methods SS I
12.3 Statistical Approaches 553
12.3.1 Parametric Methods SS3
12.3.2 Nonpârametnc Methods 5S8
114 Proxlmlty-Bued Approaches 560
12.4.1 Distance-Based Outlier Detection and a Nested Loop
Method 561
12.4.2 A Grid-Based Method 562
114.3 Density-Based Outlier Detection S64
125 Clustering-Based Approaches 567
116 Classification-Based Approaches 571
117 Mining Contextual and Collective Outliers 573
117.1 Transforming Contextual Outlier Detection to Conventional
Outlier Detection 573
«vii! Contenu
12.7.2
12.7. 3 Mining Cc Vctr««* CK **r* S *!
128 Outlier Detection In HlgtvOlrmmiooAl Data $76
ì 2.8 t Extending Convrfifcorvil OÄ«? tvtreton S T7
i 2,8 2 ? ine ng Outfier•» io SufcwîMc« S *3
12.8 3 Modeling H.^..( mrm ôfwi Ovt iprt S 3
12.9 Summary SSI
t2JO ExertUe« S82
tilt Bibllof raphlc Not« SU
Oupter 13 Data Mining Trends and Research Frontiers SIS
( 11 Mining Complex Data type« SIS
I j. I, ( Mming Sequence D-tU. Tme-Sc««, S-#« »»c 5»rrj«jrfif.rv
and Bioto£*oî Seq ^rncr5 536
13.!.2 Mtning Graph* andNctMjrVs 5 ?
13.1.3 Mming Other Ki*vh oí D.*U 5*?5
132 Other Methodologies of Data Mining St•
13.2.1 Sumticil Da» Mifuñg 5^8
(3.2.2 V*w5 on Dit» Krufvg feundrt.005 éqq
13.2.3 Visai! and Audio Ddti M,n.ng 602
13 3 Data Mining Applications 407
13.3.1 Data Mining for Ftmntwt! D^t* A^/wi 60?
13.3.2 Data Mining for Retó *xJ Tcfccetirru^iicaîori Sndur.tr éî 609
13.3.3 Data Mining « Science and Ènginçcnrig 6 •
13.3.4 Data Mining for !rvtru$ on Detection and Pvc«çriîior» 614
13.3.5 Data Mining and R«emmerrcfer Systems 61S
134 Data Mining and Society 618
13,4.1 Ubiquitous and fnvisifafe Data Mming fe I %
13.4.2 Privacy, Sccurtty. and Social Impacts of Ü U Mrsng 620
135 Dau Mining Trends 622
13.6 Summary 625
137 Exercises 626
138 Bibliographic Notes 628
Bibliography 633
Index 673
|
| any_adam_object | 1 |
| author | Han, Jiawei 1949- Kamber, Micheline Pei, Jian |
| author_GND | (DE-588)137798342 (DE-588)1146286937 (DE-588)1021437980 |
| author_facet | Han, Jiawei 1949- Kamber, Micheline Pei, Jian |
| author_role | aut aut aut |
| author_sort | Han, Jiawei 1949- |
| author_variant | j h jh m k mk j p jp |
| building | Verbundindex |
| bvnumber | BV037435337 |
| classification_rvk | QH 500 SK 850 ST 265 ST 270 ST 530 |
| classification_tum | DAT 450f |
| ctrlnum | (OCoLC)762019247 (DE-599)GBV636530418 |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| edition | 3. ed. |
| format | Book |
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| id | DE-604.BV037435337 |
| illustrated | Illustrated |
| indexdate | 2025-02-20T06:41:42Z |
| institution | BVB |
| isbn | 9780123814791 |
| language | English |
| lccn | 2011010635 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-022587349 |
| oclc_num | 762019247 |
| open_access_boolean | |
| owner | DE-29T DE-1050 DE-861 DE-863 DE-BY-FWS DE-634 DE-20 DE-91G DE-BY-TUM DE-355 DE-BY-UBR DE-2070s DE-739 DE-703 DE-384 DE-573 DE-824 DE-19 DE-BY-UBM DE-11 DE-91 DE-BY-TUM DE-473 DE-BY-UBG DE-521 DE-862 DE-BY-FWS DE-B768 DE-83 DE-945 DE-1049 DE-858 DE-522 DE-1051 |
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| physical | XXXV, 703 S. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Elsevier MK |
| record_format | marc |
| series2 | The Morgan Kaufmann series in data management systems |
| spellingShingle | Han, Jiawei 1949- Kamber, Micheline Pei, Jian Data mining concepts and techniques Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Data mining concepts and techniques |
| title_auth | Data mining concepts and techniques |
| title_exact_search | Data mining concepts and techniques |
| title_full | Data mining concepts and techniques Jiawei Han ; Micheline Kamber ; Jian Pei |
| title_fullStr | Data mining concepts and techniques Jiawei Han ; Micheline Kamber ; Jian Pei |
| title_full_unstemmed | Data mining concepts and techniques Jiawei Han ; Micheline Kamber ; Jian Pei |
| title_short | Data mining |
| title_sort | data mining concepts and techniques |
| title_sub | concepts and techniques |
| topic | Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Mining |
| url | http://www.gbv.de/dms/bowker/toc/9780123748560.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022587349&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hanjiawei dataminingconceptsandtechniques AT kambermicheline dataminingconceptsandtechniques AT peijian dataminingconceptsandtechniques |
Indexes
Indexes
Indexes
THWS Schweinfurt Zentralbibliothek Lesesaal
| Call Number: |
2000 ST 530 H233(3) |
|---|---|
| Copy 1 | ausleihbar Checked out – Due: 31.12.2099 Recall This |
THWS Würzburg Teilbibliothek SHL, Raum I.2.11
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1340 ST 530 H233(3) |
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| Copy 1 | ausleihbar Available Place a Hold |