Principles of data mining:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Springer
2013
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Undergraduate Topics in Computer Science
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025509989&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | XIV, 440 S. graph. Darst. |
| ISBN: | 9781447148838 |
Internformat
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| 264 | 1 | |a London |b Springer |c 2013 | |
| 300 | |a XIV, 440 S. |b graph. Darst. | ||
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Datensatz im Suchindex
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|---|---|
| DE-BY-TUM_katkey | 1925335 |
| DE-BY-TUM_location | 01 |
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| adam_text | Contents
1. Introduction to Data Mining ...................................... 1
1.1 The Data Explosion........................................... 1
1.2 Knowledge Discovery.......................................... 2
1.3 Applications of Data Mining.................................. 3
1.4 Labelled and Unlabelled Data............................... 4
1.5 Supervised Learning; Classification , ....................... 5
1.6 Supervised Learning: Numerical Prediction ................... 7
1.7 Unsupervised Learning: Association Rules ................... 7
1.8 Unsupervised Learning: Clustering......................... 8
2* Data for Data Minting.....................*....................... 9
2.1 Standard Formulation...................................... 9
2.2 Types of Variable...................................... 10
2.2.1 Categorical and Continuous Attributes................ 12
2.3 Data Preparation — *....................................... 12
2.3.1 Data Cleaning...................................... 13
2.4 Missing Values.......................................... 15
2.4.1 Discard Instances................................ 15
2.4.2 Replace by Most Frequent/Average Value............... 15
2.5 Reducing the Number of Attributes......................... 16
2.6 The UCI Repository of Datasets.....,....................... 17
2.7 Chapter Summary............................................. 18
2.8 Self-assessment Exercises for Chapter 2 .................. 18
Reference...................................................... 19
vii
Principles of Data Mining
viti
3. Introduction to Classification: Naïve Bayes and Nearest
Neighbour.......................................................... 21
3.1 What Is Classification?...................................... 21
3.2 Naïve Bayes Classifiers...................................... 22
3.3 Nearest Neighbour Classification............................. 29
3.3.1 Distance Measures.................................... 32
3.3.2 Normalisation......................................... 35
3.3.3 Dealing with Categorical Attributes.................. 36
3.4 Eager and Lazy Learning...................................... 36
3.5 Chapter Summary...................*......................... 37
3.6 Self-assessment Exercises for Chapter 3...................... 37
4. Using Decision Trees for Classification........................... 39
4.1 Decision Rules and Decision Trees............................ 39
4.1.1 Decision Trees: The Golf Example...................... 40
4.1.2 Terminology........................................... 41
4.1.3 The degrees Dataset .................................. 42
4.2 The TDIDT Algorithm.......................................... 45
4.3 Types of Reasoning........................................... 47
4.4 Chapter Summary.............................................. 48
4.5 Self-assessment Exercises for Chapter 4...................... 48
References........................................................ 48
5. Decision Tree Induction: Using Entropy for Attribute
Selection ........................................................ 49
5.1 Attribute Selection: An Experiment.......................... . 49
5.2 Alternative Decision Trees ................ *............... 50
5.2.1 The Football/Netball Example ........................ 51
5.2.2 The anonymous Dataset............................... 53
5.3 Choosing Attributes to Split On: Using Entropy ............. 54
5.3.1 The Iens24 Dataset ....................... 55
5.3.2 Entropy ............................................. 57
5.3.3 Using Entropy for Attribute Selection ................ 58
5.3.4 Maximising Information Gain.......................... . 60
5.4 Chapter Summary........................................ 61
5.5 Self-assessment Exercises for Chapter 5...................... 61
6. Decision Tree Induction: Using Frequency Tables
for Attribute Selection.......................................... 63
6.1 Calculating Entropy in Practice........................... 63
6.1.1 Proof of Equivalence .............................. 64
6.1.2 A Note on Zeros.........................,. ........ 66
Contents ix
6.2 Other Attribute Selection Criteria: Gini Index of Diversity .... 66
6.3 The x2 Attribute Selection Criterion........................ 68
6.4 Inductive Bias.............................................. 71
6.5 Using Gain Ratio for Attribute Selection ................... 73
6.5.1 Properties of Split Information ...................... 74
6.5.2 Summary . ............................................ 75
6.6 Number of Rules Generated by Different Attribute Selection
Criteria.................................................... 75
6.7 Missing Branches........................................ 76
6.8 Chapter Summary............................................ 77
6.9 Self-assessment Exercises for Chapter 6..................... 77
References........................................................ 78
7. Estimating the Predictive Accuracy of a Classifier................ 79
7.1 Introduction................................................ 79
7.2 Method 1: Separate Training and Test Sets .................. 80
7.2.1 Standard Error........................................ 81
7.2.2 Repeated Train and Test............................... 82
7.3 Method 2: fc-fold Cross-validation.......................... 82
7.4 Method 3: IV-fold Cross-validation.......................... 83
7.5 Experimental Results I...................................... 84
7.6 Experimental Results II: Datasets with Missing Values....... 86
7.6.1 Strategy 1: Discard Instances ........................ 87
7.6.2 Strategy 2: Replace by Most Frequent/Aver age Value . . 87
7.6.3 Missing Classifications............................. 89
7.7 Confusion Matrix.......................................... 89
7.7.1 True and False Positives........................... 90
7.8 Chapter Summary ........................................ 91
7.9 Self-assessment Exercises for Chapter 7 . ............. 91
Reference ........................................................ 92
8. Continuous Attributes ........................................... 93
8.1 Introduction................................................ 93
8.2 Local versus Global Discretisation.......................... 95
8.3 Adding Local Discretisation to TDIDT........................ 96
8.3.1 Calculating the Information Gain of a Set of Pseudo-
attributes .................................................. 97
8.3.2 Computational Efficiency..............................102
8.4 Using the ChiMerge Algorithm for Global Discretisation......105
8.4.1 Calculating the Expected Values and x2 . . . .........108
8.4.2 Finding the Threshold Value...........................113
8.4.3 Setting minlntervals and maxlntervals ................ 113
x Principles of Data Mining
8.4.4 The ChiMerge Algorithm: Summary ...................... . 115
8.4.5 The ChiMerge Algorithm: Comments......................115
8.5 Comparing Global and Local Discretisation for Tree Induction 116
8.6 Chapter Summary .......................,.................... 118
8.7 Self-assessment Exercises for Chapter 8.....................118
Reference . .................................................... 119
9. Avoiding Overfittlng of Decision Ttees ......................... 121
9-1 Dealing with Clashes in a Training Set ................... 122
9.1.1 Adapting TDIDT to Deal with Clashes ..................122
9.2 More About Overfitting Rules to Data........................127
9.3 Pro-pruning Decision Trees .................................128
9.4 Post-pruning Decision Trees.................................130
9.5 Chapter Summary ............................................135
9.6 Self-assessment Exercise for Chapter 9......................136
References........................................................136
10. More About Entropy.............................................. 137
10.1 Introduction................................................137
10.2 Coding Information Using Bits...............................140
10.3 Discriminating Amongst M Values (M Not a Power of 2)........142
10.4 Encoding Values That Are Not Equally Likely.................143
10.5 Entropy of a Training Set...................................146
10.6 Information Gain Must Be Positive or Zero . ................147
10.7 Using Information Gain for Feature Reduction
for Classification Tasks ................................. 149
10.7.1 Example 1: The genetics Dataset .......150
10.7.2 Example 2: The bcsi96 Dataset 154
10.8 Chapter Summary ........................................ 156
10.9 Self-assessment Exercises for Chapter 10 « . 156
References................................................. 156 11
11. Inducing Modular Rules for Classification .,................ 157
11.1 Rule Post-pruning ..................................... 157
11.2 Conflict Resolution........................................ 159
11.3 Problems with Decision Trees...............*................162
11.4 The Prism Algorithm..........................................164
11.4.1 Changes to the Basic Prism Algorithm.................171
11.4.2 Comparing Prism with TDIDT.......................- . - 172
11.5 Chapter Summary .......................................... 173
11.6 Self-assessment Exercise for Chapter 11................... 173
References.............*...............*............. 174
Contents
XI
12. Measuring the Performance of a Classifier..........................175
12.1 True and False Positives and Negatives........................176
12.2 Performance Measures..........................................178
12.3 True and False Positive Rates versus Predictive Accuracy.....181
12.4 ROC Graphs....................................................182
12.5 ROC Curves....................................................184
12.6 Finding the Best Classifier...................................185
12.7 Chapter Summary...............................................186
12.8 Self-assessment Exercise for Chapter 12.......................187
13. Dealing with Large Volumes of Data.................................189
13.1 Introduction..................................................189
13.2 Distributing Data onto Multiple Processors....................192
13.3 Case Study: PMCRI.............................................194
13.4 Evaluating the Effectiveness of a Distributed System: PMCRI . 197
13.5 Revising a Classifier Incrementally...........................201
13.6 Chapter Summary...............................................207
13.7 Self-assessment Exercises for Chapter 13......................207
References .................................................208
14. Ensemble Classification..........................................209
14.1 Introduction..................................................209
14.2 Estimating the Performance of a Classifier....................212
14.3 Selecting a Different Training Set for Each Classifier........213
14.4 Selecting a Different Set of Attributes for Each Classifier ..214
14.5 Combining Classifications: Alternative Voting Systems ........215
14.6 Parallel Ensemble Classifiers............................... 219
14.7 Chapter Summary............................... -.............219
14.8 Self֊assessment Exercises for Chapter 14 . ............... 220
References ............................................... 220
15. Comparing Classifiers. ......................................... 221
15.1 Introduction............................................ 221
15.2 The Paired t-Test............................................223
15.3 Choosing Datasets for Comparative Evaluation..................229
15.3.1 Confidence Intervals ..................................231
15.4 Sampling .....................................................231
15.5 How Bad Is a ‘No Significant Difference5 Result?..............234
15.6 Chapter Summary............................................. 235
15.7 Self-assessment Exercises for Chapter 15......................235
References ............................................. 236
Principles of Data Mining
xii
16. Association Rule Mining I..........................................237
16.1 Introduction..................................................237
16.2 Measures of Rule Interestingness..............................239
16.2.1 The Piatetsky-Shapiro Criteria and the RI Measure .... 241
16.2.2 Rule Interestingness Measures Applied to the chess
Dataset .............................................. 243
16.2.3 Using Rule Interestingness Measures for Conflict
Resolution....................................... 245
16.3 Association Rule Mining Tasks.................................245
16.4 Finding the Best N Rules .................................. 246
16.4.1 The J-Measure: Measuring the Information Content
of a Rule..............................................247
16.4.2 Search Strategy.......................................248
16.5 Chapter Summary...............................................251
16.6 Self-assessment Exercises for Chapter 16......................251
References.........................................................251
17. Association Rule Mining II.........................................253
17.1 Introduction .................................................253
17.2 Transactions and Itemsets.....................................254
17.3 Support for an Itemset........................................255
17.4 Association Rules.............................................256
17.5 Generating Association Rules..................................258
17.6 Apriori.......................................................259
17.7 Generating Supported Itemsets: An Example . ................ 262
17.8 Generating Rules for a Supported Itemset ................... 264
17.9 Rule Interestingness Measures: Lift and Leverage..............266
17.10 Chapter Summary ........ · 268
17.11 Self-assessment Exercises for Chapter 17 ............. *....269
Reference........................................................ 269
18. Association Rule Mining III: Frequent Pattern ’Trees ....... 271
18.1 Introduction: FP-Growth .................................. 271
18.2 Constructing the FP-tree.................................. 274
18.2.1 Pre-processing the Transaction Database ..............274
18.2.2 Initialisation....................................... . 276
18.2.3 Processing Transaction 1: ƒ, c, a, m, p.............. . 277
18.2.4 Processing Transaction 2: ƒ, c, ay b, m..............279
18.2.5 Processing Transaction 3: ƒ, b . ......... . ........283
18.2.6 Processing Transaction 4: c, 5, p. ........... 285
18.2.7 Processing Transaction 5: ƒ, e, a, m p.......... 287
18.3 Finding the Frequent Itemsets from the FP-tree ..........-----288
Contents
xiii
18.3.1 Itemsets Ending with Item p.........................291
18.3.2 Itemsets Ending with Item m .......................301
18.4 Chapter Summary............................................308
18.5 Self-assessment Exercises for Chapter 18...................309
Reference...................................................... 309
19. Clustering......................., .............................311
19.1 Introduction............................................... 311
19.2 fc-Means Clustering . .................................. 314
19.2.1 Example . ........................................ 315
19.2.2 Finding the Best Set of Clusters....................319
19.3 Agglomerative Hierarchical Clustering.......................320
19.3.1 Recording the Distance Between Clusters.............323
19.3.2 Terminating the Clustering Process................. 326
19.4 Chapter Summary.............................................327
19.5 Self-assessment Exercises for Chapter 19....................327
20. Text Mining......................................................329
20.1 Multiple Classifications....................................329
20.2 Representing Text Documents for Data Mining.................330
20.3 Stop Words and Stemming.....................................332
20.4 Using Information Gain for Feature Reduction ...............333
20.5 Representing Text Documents: Constructing a Vector Space
Model..................................................... 333
20.6 Normalising the Weights................................... . 335
20-7 Measuring the Distance Between Two Vectors................ 336
20.8 Measuring the Performance of a Text Classifier....... 337
20.9 Hypertext Categorisation ............................... 338
20.9.1 Classifying Web Pages . ............................338
20.9.2 Hypertext Classification versus Text Classification.339
20.10 Chapter Summary ....................................... 343
20.11 Self-assessment Exercises for Chapter 20..................343
A, Essential Mathematics.......................................... 345
A.l Subscript Notation. ...................................... 345
A. 1.1 Sigma Notation for Summation.........................346
A. 1.2 Double Subscript Notation............................347
A.1.3 Other Uses of Subscripts..............................348
A.2 Trees................................................... 348
A.2.1 Terminology......................................... 349
A.2.2 Interpretation...................................... 350
A.2.3 Subtrees..............................................351
XIV
Principles of Data Mining
A.3 The Logarithm Function log2 X . . . , *.......... » , ....351
A.3.1 The Function —X log2 , , . . ...................354
A.4 Introduction to Set Theory.............................. . . . 355
A.4.1 Subsets. ,.........................................357
A.4.2 Summary of Set Notation......................... 359
B. Datasets................................ ............ , ....... 361
References ............................................ . »...381
C. Sources of Further Information.........., . . ,...............383
Websites..................................................... 383
Books........................................................ 383
Books on Neural Nets......................................... 384
Conferences................................................. 385
Information About Association Rule Mining......................385
D. Glossary and Notation..........................................387
E. Solutions to Self-assessment Exercises ........................407
Index
435
|
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| title | Principles of data mining |
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| title_fullStr | Principles of data mining Max Bramer |
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Teilbibliothek Mathematik & Informatik
| Signatur: |
0102 DAT 703f 2008 A 3634(2) Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |