Data mining: practical machine learning tools and techniques
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier
2005
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | The Morgan Kaufmann series in data management systems
|
| Schlagworte: | |
| Online-Zugang: | Table of contents only Publisher description Inhaltsverzeichnis |
| Beschreibung: | XXXI, 525 S. Ill., graph. Darst. |
| ISBN: | 0120884070 9780120884070 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV019818196 | ||
| 003 | DE-604 | ||
| 005 | 20201002 | ||
| 007 | t | ||
| 008 | 050523s2005 ad|| |||| 00||| eng d | ||
| 020 | |a 0120884070 |9 0-12-088407-0 | ||
| 020 | |a 9780120884070 |9 978-0-12-088407-0 | ||
| 035 | |a (OCoLC)882084577 | ||
| 035 | |a (DE-599)BVBBV019818196 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-384 |a DE-91G |a DE-703 |a DE-91 |a DE-355 |a DE-473 |a DE-1051 |a DE-1028 |a DE-92 |a DE-N2 |a DE-19 |a DE-29T |a DE-Eb1 |a DE-83 |a DE-11 |a DE-525 |a DE-523 |a DE-188 |a DE-2070s | ||
| 050 | 0 | |a QA76.9.D343 | |
| 082 | 0 | |a 006.3 22 | |
| 082 | 0 | |a 005.741 |2 22 | |
| 084 | |a CM 4400 |0 (DE-625)18955: |2 rvk | ||
| 084 | |a QH 500 |0 (DE-625)141607: |2 rvk | ||
| 084 | |a QP 345 |0 (DE-625)141866: |2 rvk | ||
| 084 | |a ST 270 |0 (DE-625)143638: |2 rvk | ||
| 084 | |a ST 271 |0 (DE-625)143639: |2 rvk | ||
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a ST 530 |0 (DE-625)143679: |2 rvk | ||
| 084 | |a DAT 708 |2 stub | ||
| 084 | |a DAT 450 |2 stub | ||
| 100 | 1 | |a Witten, Ian H. |d 1947- |e Verfasser |0 (DE-588)138440166 |4 aut | |
| 245 | 1 | 0 | |a Data mining |b practical machine learning tools and techniques |c Ian H. Witten ; Eibe Frank |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2005 | |
| 300 | |a XXXI, 525 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a The Morgan Kaufmann series in data management systems | |
| 650 | 4 | |a Data Mining | |
| 650 | 4 | |a Data mining | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Java |g Programmiersprache |0 (DE-588)4401313-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Weka 3 |0 (DE-588)1126597503 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 651 | 7 | |a Java |0 (DE-588)4028527-3 |2 gnd |9 rswk-swf | |
| 689 | 0 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 2 | |a Weka 3 |0 (DE-588)1126597503 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 689 | 1 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 1 | 1 | |a Java |0 (DE-588)4028527-3 |D g |
| 689 | 1 | |8 2\p |5 DE-604 | |
| 689 | 2 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 2 | 1 | |a Java |g Programmiersprache |0 (DE-588)4401313-9 |D s |
| 689 | 2 | |8 3\p |5 DE-604 | |
| 700 | 1 | |a Frank, Eibe |e Verfasser |0 (DE-588)122539044 |4 aut | |
| 856 | 4 | 2 | |q text/html |u http://www.loc.gov/catdir/enhancements/fy0624/2005043385-t.html |3 Table of contents only |
| 856 | 4 | 2 | |q text/html |u http://www.loc.gov/catdir/enhancements/fy0624/2005043385-d.html |3 Publisher description |
| 856 | 4 | 2 | |m HBZ Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013143510&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 2\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 3\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-013143510 | |
Datensatz im Suchindex
| _version_ | 1805087025714429952 |
|---|---|
| adam_text |
Titel: Data mining
Autor: Witten, Ian H.
Jahr: 2005
Contents
Foreword v
Preface xxiii
Updated and revised content xxvii
Acknowledgments xxix
Part I Machine learning tools and techniques 1
1 What's it all about? 3
1.1 Data mining and machine learning 4
Describing structural patterns 6
Machine learning 7
Data mining 9
1.2 Simple examples: The weather problem and others 9
The weather problem 10
Contact lenses: An idealized problem 13
Irises: A classic numeric dataset 15
CPU performance: Introducing numeric prediction 16
Labor negotiations: A more realistic example 17
Soybean classification: A classic machine learning success 18
1.3 Fielded applications 22
Decisions involving judgment 22
Screening images 23
Load forecasting 24
Diagnosis 25
Marketing and sales 26
Other applications 28
CONTENTS
1.4 Machine learning and statistics 29
1.5 Generalization as search 30
Enumerating the concept space 31
Bias 32
1.6 Data mining and ethics 35
1.7 Further reading 37
L Input: Concepts, instances, and attributes 41
2.1 What's a concept? 42
2.2 What's in an example? 45
2.3 What's in an attribute? 49
2.4 Preparing the input 52
Gathering the data together 52
ARFF format 53
Sparse data 55
Attribute types 56
Missing values 58
Inaccurate values 59
Getting to know your data 60
2.5 Further reading 60
3 Output Knowledge representation 61
3.1 Decision tables 62
3.2 Decision trees 62
3.3 Classification rules 65
3.4 Association rules 69
3.5 Rules with exceptions 70
3.6 Rules involving relations 73
3.7 Trees for numeric prediction 76
3.8 Instance-based representation 76
3.9 Clusters 81
3.10 Further reading 82
CONTENTS IX
4 Algorithms: The basic methods 83
4.1 Inferring rudimentary rules 84
Missing values and numeric attributes 86
Discussion 88
4.2 Statistical modeling 88
Missing values and numeric attributes 92
Bayesian models for document classification 94
Discussion 96
4.3 Divide-and-conquer: Constructing decision trees 97
Calculating information 100
Highly branching attributes 102
Discussion 105
4.4 Covering algorithms: Constructing rules 105
Rules versus trees 107
A simple covering algorithm 107
Rules versus decision lists 111
4.5 Mining association rules 112
Item sets 113
Association rules 113
Generating rules efficiently 117
Discussion 118
4.6 Linear models 119
Numeric prediction: Linear regression 119
Linear classification: Logistic regression 121
Linear classification using the perceptron 124
Linear classification using Winnow 126
4.7 Instance-based learning 128
The distance function 128
Finding nearest neighbors efficiently 129
Discussion 135
4.8 Clustering 136
Iterative distance-based clustering 137
Faster distance calculations 138
Discussion 139
4.9 Further reading 139
CONTENTS
5 Credibility: Evaluating what's been learned 143
5.1 Training and testing 144
5.2 Predicting performance 146
5.3 Cross-validation 149
5.4 Other estimates 151
Leave-one-out 151
The bootstrap 152
5.5 Comparing data mining methods 153
5.6 Predicting probabilities 157
Quadratic loss function 158
Informational loss function 159
Discussion 160
5.7 Counting the cost 161
Cost-sensitive classification 164
Cost-sensitive learning 165
Lift charts 166
ROC curves 168
Recall-precision curves 171
Discussion 172
Cost curves 173
5.8 Evaluating numeric prediction 176
5.9 The minimum description length principle 179
5.10 Applying the MDL principle to clustering 183
5.11 Further reading 184
6 Implementations: Real machine learning schemes 187
6.1 Decision trees 189
Numeric attributes 189
Missing values 191
Pruning 192
Estimating error rates 193
Complexity of decision tree induction 196
From trees to rules 198
C4.5: Choices and options 198
Discussion 199
6.2 Classification rules 200
Criteria for choosing tests 200
Missing values, numeric attributes 201
CONTENTS XI
Generating good rules 202
Using global optimization 205
Obtaining rules from partial decision trees 207
Rules with exceptions 210
Discussion 213
6.3 Extending linear models 214
The maximum margin hyperplane 215
Nonlinear class boundaries 217
Support vector regression 219
The kernel perceptron 222
Multilayer perceptrons 223
Discussion 235
6.4 Instance-based learning 235
Reducing the number of exemplars 236
Pruning noisy exemplars 236
Weighting attributes 237
Generalizing exemplars 238
Distance functions for generalized exemplars 239
Generalized distance functions 241
Discussion 242
6.5 Numeric prediction 243
Model trees 244
Building the tree 245
Pruning the tree 245
Nominal attributes 246
Missing values 246
Pseudocode for model tree induction 247
Rules from model trees 250
Locally weighted linear regression 251
Discussion 253
6.6 Clustering 254
Choosing the number of clusters 254
Incremental clustering 255
Category utility 260
Probability-based clustering 262
The EM algorithm 265
Extending the mixture model 266
Bayesian clustering 268
Discussion 270
6.7 Bayesian networks 271
Making predictions 272
Learning Bayesian networL· 276
XU CONTENTS
Specific algorithms 278
Data structures for fast learning 280
Discussion 283
7 Transformations: Engineering the input and output 285
7.1 Attribute selection 288
Scheme-independent selection 290
Searching the attribute space 292
Scheme-specific selection 294
7.2 Discretizing numeric attributes 296
Unsupervised discretization 297
Entropy-based discretization 298
Other discretization methods 302
Entropy-based versus error-based discretization 302
Converting discrete to numeric attributes 304
7.3 Some useful transformations 305
Principal components analysis 306
Random projections 309
Text to attribute vectors 309
Time series 311
7.4 Automatic data cleansing 312
Improving decision trees 312
Robust regression 313
Detecting anomalies 314
7.5 Combining multiple models 315
Bagging 316
Bagging with costs 319
Randomization 320
Boosting 321
Additive regression 325
Additive logistic regression 327
Option trees 328
Logistic model trees 331
Stacking 332
Error-correcting output codes 334
7.6 Using unlabeled data 337
Clustering for classification 337
Co-training 339
EM and co-training 340
7.7 Further reading 341
CONTENTS XIII
8 Moving on: Extensions and applications 345
8.1 Learning from massive datasets 346
8.2 Incorporating domain knowledge 349
8.3 Text and Web mining 351
8.4 Adversarial situations 356
8.5 Ubiquitous data mining 358
8.6 Further reading 361
Part II The Weka machine learning workbench 363
9 Introduction to Weka 365
9.1 What's in Weka? 366
9.2 How do you use it? 367
9.3 What else can you do? 368
9.4 How do you get it? 368
10 The Explorer 369
10.1 Getting started 369
Preparing the data 370
Loading the data into the Explorer 370
Building a decision tree 373
Examining the output 373
Doing it again 377
Working with models 377
When things go wrong 378
10.2 Exploring the Explorer 380
Loading and filtering files 380
Training and testing learning schemes 384
Do it yourself: The User Classifier 388
Using a metalearner 389
Clustering and association rules 391
Attribute selection 392
Visualization 393
10.3 Filtering algorithms 393
Unsupervised attribute filters 395
Unsupervised instance filters 400
Supervised filters 401
XIV CONTENTS
10.4 Learning algorithms 403
Bayesian classifiers 403
Trees 406
Rules 408
Functions 409
Lazy classifiers 413
Miscellaneous classifiers 414
10.5 Metalearning algorithms 414
Bagging and randomization 414
Boosting 416
Combining classifiers 417
Cost-sensitive learning 417
Optimizing performance 417
Retargeting classifiers for different tasL· 418
10.6 Clustering algorithms 418
10.7 Association-rule learners 419
10.8 Attribute selection 420
Attribute subset evaluators 422
Single-attribute evaluators 422
Search methods 423
11 The Knowledge Flow interface 427
11.1 Getting started 427
11.2 The Knowledge Flow components 430
11.3 Configuring and connecting the components 431
11.4 Incremental learning 433
12 The Experimenter 437
12.1 Getting started 438
Running an experiment 439
Analyzing the results 440
12.2 Simple setup 441
12.3 Advanced setup 442
12.4 The Analyze panel 443
12.5 Distributing processing over several machines 445
CONTENTS XV
13 The command-line interface 449
13.1 Getting started 449
13.2 The structure of Weka 450
Classes, instances, and packages 450
The wekaxore package 451
The wekaxlassifiers package 453
Other packages 455
Javadoc indices 456
13.3 Command-line options 456
Generic options 456
Scheme-specific options 458
14 Embedded machine learning 461
14.1 A simple data mining application 461
14.2 Going through the code 462
main() 462
MessageClassifierO 462
updateDataO 468
classifyMessage() 468
15 Writing new learning schemes 471
15.1 An example classifier 471
buildClassifierO 472
makeTree() 472
computelnfoGainQ 480
classifylnstanceQ 480
ntainQ 481
15.2 Conventions for implementing classifiers 483
References 485
Index 505
About the authors , 525 |
| any_adam_object | 1 |
| author | Witten, Ian H. 1947- Frank, Eibe |
| author_GND | (DE-588)138440166 (DE-588)122539044 |
| author_facet | Witten, Ian H. 1947- Frank, Eibe |
| author_role | aut aut |
| author_sort | Witten, Ian H. 1947- |
| author_variant | i h w ih ihw e f ef |
| building | Verbundindex |
| bvnumber | BV019818196 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.9.D343 |
| callnumber-search | QA76.9.D343 |
| callnumber-sort | QA 276.9 D343 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | CM 4400 QH 500 QP 345 ST 270 ST 271 ST 300 ST 530 |
| classification_tum | DAT 708 DAT 450 |
| ctrlnum | (OCoLC)882084577 (DE-599)BVBBV019818196 |
| dewey-full | 006.322 005.741 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods 005 - Computer programming, programs, data, security |
| dewey-raw | 006.3 22 005.741 |
| dewey-search | 006.3 22 005.741 |
| dewey-sort | 16.3 222 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Psychologie Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV019818196</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20201002</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">050523s2005 ad|| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0120884070</subfield><subfield code="9">0-12-088407-0</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780120884070</subfield><subfield code="9">978-0-12-088407-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)882084577</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV019818196</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-91G</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-1051</subfield><subfield code="a">DE-1028</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-N2</subfield><subfield code="a">DE-19</subfield><subfield code="a">DE-29T</subfield><subfield code="a">DE-Eb1</subfield><subfield code="a">DE-83</subfield><subfield code="a">DE-11</subfield><subfield code="a">DE-525</subfield><subfield code="a">DE-523</subfield><subfield code="a">DE-188</subfield><subfield code="a">DE-2070s</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA76.9.D343</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3 22</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">005.741</subfield><subfield code="2">22</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">CM 4400</subfield><subfield code="0">(DE-625)18955:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 500</subfield><subfield code="0">(DE-625)141607:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QP 345</subfield><subfield code="0">(DE-625)141866:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 270</subfield><subfield code="0">(DE-625)143638:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 271</subfield><subfield code="0">(DE-625)143639:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 530</subfield><subfield code="0">(DE-625)143679:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 708</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 450</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Witten, Ian H.</subfield><subfield code="d">1947-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)138440166</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data mining</subfield><subfield code="b">practical machine learning tools and techniques</subfield><subfield code="c">Ian H. Witten ; Eibe Frank</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">2. ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Amsterdam [u.a.]</subfield><subfield code="b">Elsevier</subfield><subfield code="c">2005</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XXXI, 525 S.</subfield><subfield code="b">Ill., graph. Darst.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">The Morgan Kaufmann series in data management systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data Mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data mining</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Java</subfield><subfield code="g">Programmiersprache</subfield><subfield code="0">(DE-588)4401313-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Weka 3</subfield><subfield code="0">(DE-588)1126597503</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="651" ind1=" " ind2="7"><subfield code="a">Java</subfield><subfield code="0">(DE-588)4028527-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Weka 3</subfield><subfield code="0">(DE-588)1126597503</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Java</subfield><subfield code="0">(DE-588)4028527-3</subfield><subfield code="D">g</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="2" ind2="0"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2="1"><subfield code="a">Java</subfield><subfield code="g">Programmiersprache</subfield><subfield code="0">(DE-588)4401313-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2=" "><subfield code="8">3\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Frank, Eibe</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)122539044</subfield><subfield code="4">aut</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="q">text/html</subfield><subfield code="u">http://www.loc.gov/catdir/enhancements/fy0624/2005043385-t.html</subfield><subfield code="3">Table of contents only</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="q">text/html</subfield><subfield code="u">http://www.loc.gov/catdir/enhancements/fy0624/2005043385-d.html</subfield><subfield code="3">Publisher description</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">HBZ Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013143510&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">3\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-013143510</subfield></datafield></record></collection> |
| geographic | Java (DE-588)4028527-3 gnd |
| geographic_facet | Java |
| id | DE-604.BV019818196 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T08:45:35Z |
| institution | BVB |
| isbn | 0120884070 9780120884070 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-013143510 |
| oclc_num | 882084577 |
| open_access_boolean | |
| owner | DE-384 DE-91G DE-BY-TUM DE-703 DE-91 DE-BY-TUM DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-1051 DE-1028 DE-92 DE-N2 DE-19 DE-BY-UBM DE-29T DE-Eb1 DE-83 DE-11 DE-525 DE-523 DE-188 DE-2070s |
| owner_facet | DE-384 DE-91G DE-BY-TUM DE-703 DE-91 DE-BY-TUM DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-1051 DE-1028 DE-92 DE-N2 DE-19 DE-BY-UBM DE-29T DE-Eb1 DE-83 DE-11 DE-525 DE-523 DE-188 DE-2070s |
| physical | XXXI, 525 S. Ill., graph. Darst. |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Elsevier |
| record_format | marc |
| series2 | The Morgan Kaufmann series in data management systems |
| spelling | Witten, Ian H. 1947- Verfasser (DE-588)138440166 aut Data mining practical machine learning tools and techniques Ian H. Witten ; Eibe Frank 2. ed. Amsterdam [u.a.] Elsevier 2005 XXXI, 525 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier The Morgan Kaufmann series in data management systems Data Mining Data mining Data Mining (DE-588)4428654-5 gnd rswk-swf Java Programmiersprache (DE-588)4401313-9 gnd rswk-swf Weka 3 (DE-588)1126597503 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Java (DE-588)4028527-3 gnd rswk-swf Data Mining (DE-588)4428654-5 s Maschinelles Lernen (DE-588)4193754-5 s Weka 3 (DE-588)1126597503 s 1\p DE-604 Java (DE-588)4028527-3 g 2\p DE-604 Java Programmiersprache (DE-588)4401313-9 s 3\p DE-604 Frank, Eibe Verfasser (DE-588)122539044 aut text/html http://www.loc.gov/catdir/enhancements/fy0624/2005043385-t.html Table of contents only text/html http://www.loc.gov/catdir/enhancements/fy0624/2005043385-d.html Publisher description HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013143510&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Witten, Ian H. 1947- Frank, Eibe Data mining practical machine learning tools and techniques Data Mining Data mining Data Mining (DE-588)4428654-5 gnd Java Programmiersprache (DE-588)4401313-9 gnd Weka 3 (DE-588)1126597503 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4401313-9 (DE-588)1126597503 (DE-588)4193754-5 (DE-588)4028527-3 |
| title | Data mining practical machine learning tools and techniques |
| title_auth | Data mining practical machine learning tools and techniques |
| title_exact_search | Data mining practical machine learning tools and techniques |
| title_full | Data mining practical machine learning tools and techniques Ian H. Witten ; Eibe Frank |
| title_fullStr | Data mining practical machine learning tools and techniques Ian H. Witten ; Eibe Frank |
| title_full_unstemmed | Data mining practical machine learning tools and techniques Ian H. Witten ; Eibe Frank |
| title_short | Data mining |
| title_sort | data mining practical machine learning tools and techniques |
| title_sub | practical machine learning tools and techniques |
| topic | Data Mining Data mining Data Mining (DE-588)4428654-5 gnd Java Programmiersprache (DE-588)4401313-9 gnd Weka 3 (DE-588)1126597503 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Data Mining Data mining Java Programmiersprache Weka 3 Maschinelles Lernen Java |
| url | http://www.loc.gov/catdir/enhancements/fy0624/2005043385-t.html http://www.loc.gov/catdir/enhancements/fy0624/2005043385-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013143510&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT wittenianh dataminingpracticalmachinelearningtoolsandtechniques AT frankeibe dataminingpracticalmachinelearningtoolsandtechniques |