Data mining algorithms: explained using R
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester
Wiley
2015
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Cover Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXXI, 683 S. Ill., graph. Darst. 252 mm |
| ISBN: | 9781118332580 |
Internformat
MARC
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| 008 | 150323s2015 ad|| |||| 00||| eng d | ||
| 010 | |a 2014036992 | ||
| 020 | |a 9781118332580 |c hardback : ca. USD 80.00 |9 978-1-118-33258-0 | ||
| 035 | |a (OCoLC)903916928 | ||
| 035 | |a (DE-599)GBV797286276 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
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| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 100 | 1 | |a Cichosz, Paweł |e Verfasser |0 (DE-588)1069425044 |4 aut | |
| 245 | 1 | 0 | |a Data mining algorithms |b explained using R |c Paweł Cichosz |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Chichester |b Wiley |c 2015 | |
| 300 | |a XXXI, 683 S. |b Ill., graph. Darst. |c 252 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | 1 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-118-95080-7 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-118-95084-5 |
| 856 | 4 | |u http://catalogimages.wiley.com/images/db/jimages/9781118332580.jpg |3 Cover | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027877623&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-027877623 | ||
Datensatz im Suchindex
| _version_ | 1804153135448457216 |
|---|---|
| adam_text | Contents
Acknowledgements
xix
Preface
xxi
References
xxxi
Part I Preliminaries
1
3
3
4
4
5
5
5
5
6
6
6
7
7
8
8
8
9
9
10
10
11
12
13
13
13
1
Tasks
1.1
Introduction
1.1.1
Knowledge
1.1.2
Inference
1.2
Inductive learning tasks
1.2.1
Domain
1.2.2
Instances
1.2.3
Attributes
1.2.4
Target attribute
1.2.5
Input attributes
1.2.6
Training set
1.2.7
Model
1.2.8
Performance
1.2.9
Generalization
1.2.10
Overfitting
L2.il
Algorithms
1.2.12
Inductive learning as search
1.3
Classification
1.3.1
Concept
1.3.2
Training set
1.3.3
Model
1.3.4
Performance
1.3.5
Generalization
1.3.6
Overfitting
1.3.7
Algorithms
viii CONTENTS
14
1.4
Regression
1.4.1
Target function
1.4.2
Training set 14
1.4.3
Model 15
I
.4.4
Performance ^
1.4.5
Generalization ^
1.4.6
Overfitting 15
1.4.7
Algorithms 16
1.5
Clustering 16
1.5.1
Motivation l6
1.5.2
Training set 17
1.5.3
Model 18
1.5.4
Crisp vs. soft clustering
18
1.5.5
Hierarchical clustering
18
1.5.6
Performance
18
1.5.7
Generalization
19
1.5.8
Algorithms
19
1.5.9
Descriptive vs. predictive clustering
19
1.6
Practical issues
19
1.6.1
Incomplete data
20
1.6.2
Noisy data
20
1
.7
Conclusion
20
1.8
Further readings
21
References
22
2
Basic statistics
23
2.
1 Introduction
23
2.2
Notational conventions
24
2.3
Basic statistics as modeling
24
2.4
Distribution description
25
2.4.1
Continuous attributes
25
2.4.2
Discrete attributes
36
2.4.3
Confidence intervals
40
2.4.4
m-Esti
mation
43
2.5
Relationship detection
47
2.5.1
Significance tests
48
2.5.2
Continuous attributes
50
2.5.3
Discrete attributes
52
2.5.4
Mixed attributes
55
2.5.5
Relationship detection caveats
61
2.6
Visualization
52
2.6.1
Boxplot 62
2.6.2
Histogram
6З
2.6.3
Barplot
64
2.7
Conclusion 65
2.8
Further readings
66
References 67
CONTENTS ix
Part II Classification
59
71
71
72
72
74
74
76
76
78
79
80
82
86
86
90
91
91
100
ΙΟΙ
103
104
104
105
106
106
ИЗ
114
114
References
1
^
Decision trees
3.1
Introduction
3.2
Decision
tree model
3.2.1
Nodes and branches
3.2.2
Leaves
3.2.3
Split types
3.3
Growing
3.3.1
Algorithm outline
3.3.2
Class distribution calculation
3.3.3
Class label assignment
3.3.4
Stop criteria
3.3.5
Split selection
3.3.6
Split application
3.3.7
Complete process
3.4
Pruning
3.4.1
Pruning operators
3.4.2
Pruning criterion
3.4.3
Pruning control strategy
3.4.4
Conversion to rule sets
3.5
Prediction
3.5.1
Class label prediction
3.5.2
Class probability prediction
3.6
Weighted instances
3.7
Missing
value handling
3.7.1
Fractional instances
3.7.2
Surrogate splits
3.8
Conclusion
3.9
Further readings
4
Naïve Bayes
classifier
4.1
Introduction
4.2
Bayes
rule
4.3
Classification by Bayesian inference
4.3.1
Conditional class probability
4.3.2
Prior class probability
4.3.3
Independence assumption
4.3.4
Conditional attribute value probabilities
4.3.5
Model construction
4.3.6
Prediction
4.4
Practical issues
4.4.1
Zero and small probabilities
18
18
18
20
20
21
22
22
23
24
25
25
4.4.2
Linear classification
-
4.4.3
Continuous attributes
CONTENTS
4.4.4
Missing attribute values
4.4.5
Reducing naivety
4.5
Conclusion
4.6
Further
readings
References
Linea
5.1
г
classification
Introduction
5.2
Linear representation
5.2.1
Inner representation function
5.2.2
Outer representation function
5.2.3
Threshold representation
5.2.4
Logit representation
5.3
Parameter estimation
5.3.1
Delta rule
5.3.2
Gradient descent
5.3.3
Distance to decision boundary
5.3.4
Least squares
5.4
Discrete attributes
5.5
Conclusion
5.6
Further
readings
128
129
131
131
132
134
134
136
137
138
139
142
145
145
149
152
153
154
155
156
References
157
Misclassification costs
159
6.
1 Introduction
159
6.2
Cost representation
161
6.2.1
Cost matrix
161
6.2.2
Per-class cost vector
162
6.2.3
Instance-specific costs
163
6.3
Incorporating misclassincation costs
164
6.3.
1 Instance weighting
164
6.3.2
Instance resampling
167
6.3.3
Minimum-cost rule
169
6.3.4
Instance relabeling
174
176
6.4
Effects of cost incorporation
6.5
Experimental procedure
6.6
Conclusion
6.7
Further readings
References
7
Classification
modei
evaluation
7.1
Introduction
7.1.1
Dataset
performance
7.1.2
Training performance
7.1.3
True performance
7.2
Performance measures
7-2.1
Misclassirication error
lo/
lg9
191
CONTENTS xi
7.2.2
Weighted misclassification error
191
7.2.3
Mean
misclassification
cost
192
7.2.4
Confusion matrix
194
7.2.5
ROC analysis
200
7.2.6
Probabilistic performance measures
210
7.3
Evaluation procedures
213
7.3.1
Model evaluation vs. modeling procedure evaluation
213
7.3.2
Evaluation caveats
214
7.3.3
Hold-out
217
7.3.4
Cross-validation
219
7.3.5
Leave-one-out
221
7.3.6
Bootstrapping
223
7.3.7
Choosing the right procedure
227
7.3.8
Evaluation procedures for temporal data
230
7.4
Conclusion
231
7.5
Further readings
232
References
233
Partili
Regression
235
8
Linear regression
237
8.1
Introduction
237
8.2
Linear representation
238
8.2.1
Parametric representation
239
8.2.2
Linear representation function
240
8.2.3
Nonlinear representation functions
241
8.3
Parameter estimation
242
8.3.1
Mean square error minimization
242
8.3.2
Delta rule
243
8.3.3
Gradient descent
245
8.3.4
Least squares
248
8.4
Discrete attributes
250
8.5
Advantages of linear models
251
8.6
Beyond linearity
252
8.6.1
Generalized linear representation
252
8.6.2
Enhanced representation
255
8.6.3
Polynomial regression
256
8.6.4
Piecewise-linear regression
257
8.7
Conclusion
-
8.8
Further readings
258
References
^
Regression trees
9.1
Introduction
261
9.2
Regression tree model
^62
9.2.1
Nodes and branches
262
xii CONTENTS
9.2.2
Leaves
9.2.3
Split types
9.2.4
Piecewise-constant regression
9.3
Growing
9.3.1
Algorithm outline
9.3.2
Target function summary statistics 265
9.3.3
Target value assignment 266
9.3.4
Stop criteria 267
9.3.5
Split selection 268
9.3.6
Split application 271
9.3.7
Complete process 272
9.4
Pruning 274
9.4.1
Pruning operators 27^
9.4.2
Pruning criterion 275
9.4.3
Pruning control strategy 277
9.5
Prediction 277
9.6
Weighted instances 2?8
9.7
Missing value handling
279
9.7.1
Fractional instances
279
9.7.2
Surrogate splits
284
9.8
Piecewise linear regression
284
9.8.
1 Growing
285
9.8.2
Pruning
289
9.8.3
Prediction
290
9.9
Conclusion
292
9.10
Further readings
292
References
293
10
Regression model evaluation
295
10.1
Introduction
295
10.1.
1
Dataset
performance
295
10.1.2
Training performance
295
10.1.3
True performance
295
10.2
Performance measures
296
10.2.1
Residuals
296
10.2.2
Mean absolute error
297
10.2.3
Mean square error
297
10.2.4
Root mean square error
299
10.2.5
Relative absolute error
299
10.2.6
Coefficient of determination
300
10.2.7
Correlation 301
10.2.8
Weighted performance measures
301
10.2.9
Loss functions
302
10.3
Evaluation procedures
303
10.3.1
Hold-out 304
10.3.2
Cross-validation
304
CONTENTS xiii
10.3.3
Leave-one-out
305
10.3.4
Bootstrapping
305
10.3.5
Choosing the right procedure
307
10.4
Conclusion
309
10.5
Further readings
309
References
310
Part IV Clustering
311
11
(Dis)similarity measures
3
1
3
11.1
Introduction
3
1
3
11.2
Measuring dissimilarity and similarity
ЗІЗ
11.3
Difference-based dissimilarity
314
11.3.1
Euclidean distance
314
11.3.2
Minkowski distance
315
11.3.3
Manhattan distance
316
11.3.4
Canberra distance
316
11.3.5
Chebyshev distance
317
11.3.6
Hamming distance
318
11.3.7
Gower
s
coefficient
318
11.3.8
Attribute weighting
320
11.3.9
Attribute transformation
320
11.4
Correlation-based similarity
321
11.4.1
Discrete attributes
322
11.4.2
Pearson s correlation similarity
322
11.4.3
Spearman s correlation similarity
323
11.4.4
Cosine similarity
323
11.5
Missing attribute values
324
11.6
Conclusion
325
11.7
Further readings
325
References
32
12
¿-Centers clustering
12.1
Introduction
12.1.1
Basic principle
328
12.1.2
(Dis)similarity measures
329
12.2
Algorithm scheme
330
12.2.1
Initialization
331
12.2.2
Stop criteria
331
12.2.3
Cluster formation
331
12.2.4
Implicit cluster modeling
332
12.2.5
Instantiations
332
12.3
/t-Means
33**
12.3.1
Center adjustment
335
12.3.2
Minimizing dissimilarity to centers
336
xiv CONTENTS
338
12.4
Beyond means
12.4.1
А
-Medians
12.4.2
¿-Medoids
12.5
Beyond (fixed)*
12.5.1
Multiple runs
12.5.2
Adaptive ¿-centers
12.6
Explicit cluster modeling
343
12.7
Conclusion JHJ
12.8
Further readings 345
References 3
1
3
Hierarchical clustering
349
13.1
Introduction
349
13.1.1
Basic approaches
349
13.1.2
(Dis)similarity measures
349
13.2
CI uster
hierarchies
351
13.2.1
Motivation
351
13.2.2
Model representation
352
13.3
Agglomerative clustering
353
13.3.1
Algorithm scheme
353
13.3.2
Cluster linkage
356
13.4
Divisive clustering
361
13.4.1
Algorithm scheme
361
13.4.2
Wrapping a flat clustering algorithm
361
13.4.3
Stop criteria
362
13.5
Hierarchical clustering visualization
364
1
3.6
Hierarchical clustering prediction
366
13.6.
1 Cutting cluster hierarchies
366
1
3.6.2
Cluster membership assignment
368
13.7
Conclusion
369
13.8
Further readings
370
References
371
14
Clustering model evaluation
373
14.1
Introduction
373
1
4.
1
.
1
Dataset
performance
373
1
4.
1
.2
Training performance
374
14.1.3
True performance
374
14.2
Ptr-cluster quality measures
376
14.2.1
Diameter
375
14.2.2
Separation
377
14.2.3
Isolation
37g
14.2.4
Silhouette width
379
14.2.5
Davies-Bouldm index 3g2
14.3
Overall quality measures 385
14.3.1
Dunn index 386
14.3.2
Average Davies-Bouldin index 3g7
CONTENTS xv
14.3.3
С
index
388
14.3.4
Average silhouette width
389
14.3.5
Loglikelihood
390
14.4
External quality measures
393
14.4.1
Misclassification error
393
14.4.2
Rand index
394
14.4.3
General relationship detection measures
396
14.5
Using quality measures
397
14.6
Conclusion
398
14.7
Further readings
398
References
399
Part V Getting Better Models
401
15
Model ensembles
403
403
404
406
406
412
412
412
418
420
420
422
424
427
431
431
433
433
443
446
15.6
Quality of ensemble predictions
448
15.7
Conclusion ****9
15.8
Further readings 450
References 4*
16
Kernel methods 454
16.1
Introduction 4^4
16.2
Support vector machines
16.2.1
Classification margin
16.2.2
Maximum-margin
hyperplane 460
16.2.3
Primal form
460
16.2.4
Dual form 464
Model
ensembles
15.1
Introduction
15.2
Model
committees
15.3
Base models
15.3.1
Different training sets
15.3.2
Different algorithms
15.3.3
Different parameter setups
15.3.4
Algorithm randomization
15.3.5
Base model diversity
15.4
Model
aggregation
15.4.1
Voting/Averaging
15.4.2
Probability averaging
15.4.3
Weighted voting/averaging
15.4.4
Using as attributes
15.5
Specific ensemble modeling algorithms
15.5.1
Bagging
15.5.2
Stacking
15.5.3
Boosting
15.5.4
Random forest
15.5.5
Random
Naïve Bayes
xvi CONTENTS
16.2.5
Soft margin
16.3
Support vector regression JJ
16.3.1
Regression tube 474
16.3.2
Primal form 475
16.3.3
Dual form 475
16.4 Kerneltrick 482
16.5
Kernel functions 484
16.5.1
Linear kernel 485
16.5.2
Polynomial kernel 485
16.5.3
Radial kernel 485
16.5.4
Sigmoid kernel 486
16.6
Kernel prediction
487
16.7
Kernel-based algorithms
489
16.7.1
Kernel-based
SVM
489
16.7.2
Kernel-based
S VR
492
16.8
Conclusion
494
16.9
Further readings
495
References
496
17
Attribute transformation
498
17.1
Introduction
498
17.2
Attribute transformation task
499
17.2.1
Target task
499
17.2.2
Target attribute
500
17.2.3
Transformed attribute
500
17.2.4
Training set
500
17.2.5
Modeling transformations
500
17.2.6
Nonmodeling transformations
503
17.3
Simple transformations
504
17.3.1
Standardization
504
17.3.2
Normalization
505
17.3.3
Aggregation
506
17.3.4
Imputation 507
17.3.5
Binary encoding
5O8
17.4
Multiclass encoding
5
q
17.4.
1 Encoding and decoding functions
5
11
17.4.2 1
-ok-* encoding 514
17.4.3
Error-correcting encoding 515
17.4.4
Effects of multiclass encoding
519
17.5
Conclusion
17.6
Further readings
References
18
Discretization
18.1
Introduction
524
18.2
Discretization task
CONTENTS xvii
18.2.1 Motivation 525
18.2.2
Task definition
526
18.2.3
Discretization as modeling
527
18.2.4
Discretization quality
529
18.3
Unsupervised discretization
530
18.3.1
Equal-width intervals
530
18.3.2
Equal-frequency intervals
531
18.3.3
Nonmodeling discretization
532
18.4
Supervised discretization
533
18.4.1
Pure-class discretization
533
18.4.2
Bottom-up discretization
535
18.4.3
Top-down discretization
546
18.5
Effects of discretization
551
18.6
Conclusion
553
18.7
Further readings
553
References
556
19
Attribute selection
558
19.1
Introduction
558
19.2
Attribute selection task
559
19.2.1
Motivation
559
19.2.2
Task definition
560
19.2.3
Algorithms
561
19.3
Attribute subset search
562
19.3.1
Search task
562
19.3.2
Initial state
563
19.3.3
Search operators
564
19.3.4
State selection
564
19.3.5
Stop criteria
565
19.4
Attribute selection filters
568
19.4.1
Simple statistical filters
568
19.4.2
Correlation-based filters
571
19.4.3
Consistency-based filters
575
19.4.4
RELIEF 577
19.4.5
Random forest 584
19.4.6
Cutoff criteria 585
19.4.7
Filter-driven search 586
19.5
Attribute selection wrappers 588
coo
19.5.1
Subset evaluation -500
19.5.2
Wrapper attribute selection
591
19.6
Effects of attribute selection 593
19.7
Conclusion
19.8
Further readings
References
xviii CONTENTS
20
Casestudies 602
602
603
603
603
605
606
608
610
616
624
628
631
632
632
634
635
636
639
640
640
641
641
644
647
649
650
654
655
655
657
659
659
659
660
B R
pockages 66
В.
I
CRAN
packages
ľľ
B.2 DMR packages
¿¿
B.3 Installing packages
References
Г?,
664
Cases
Judies
20.1
Introduction
20.1.1
Datasets
20.1.2
Packages
20.1.3
Auxiliary functions
20.2
Census
і
income
20.2.1
Data loading and preprocessing
20.2.2
Default model
20.2.3
Incorporating misclassification costs
20.2.4
Pruning
20.2.5
Attribute selection
20.2.6
Final models
20.3
Communities and crime
20.3.1
Data loading
20.3.2
Data quality
20.3.3
Regression trees
20.3.4
Linear models
20.3.5
Attribute selection
20.3.6
Piecewise-linear models
20.4
Cover type
20.4.1
Data loading and preprocessing
20.4.2
Class imbalance
20.4.3
Decision trees
20.4.4
Class rebalancing
20.4.5
Multiclass encoding
20.4.6
Final classification models
20.4.7
Clustering
20.5
Conclusion
20.6
Further readings
References
ing
Notation
A.I
Attribute values
A.2
Data subsets
A.3
Probabilities
666
Index
667
|
| any_adam_object | 1 |
| author | Cichosz, Paweł |
| author_GND | (DE-588)1069425044 |
| author_facet | Cichosz, Paweł |
| author_role | aut |
| author_sort | Cichosz, Paweł |
| author_variant | p c pc |
| building | Verbundindex |
| bvnumber | BV042442351 |
| classification_rvk | QH 235 ST 530 ST 601 |
| ctrlnum | (OCoLC)903916928 (DE-599)GBV797286276 |
| dewey-full | 006.3/12 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/12 |
| dewey-search | 006.3/12 |
| dewey-sort | 16.3 212 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV042442351 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:21:48Z |
| institution | BVB |
| isbn | 9781118332580 |
| language | English |
| lccn | 2014036992 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027877623 |
| oclc_num | 903916928 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-703 DE-739 DE-945 DE-824 DE-19 DE-BY-UBM |
| owner_facet | DE-473 DE-BY-UBG DE-703 DE-739 DE-945 DE-824 DE-19 DE-BY-UBM |
| physical | XXXI, 683 S. Ill., graph. Darst. 252 mm |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Wiley |
| record_format | marc |
| spelling | Cichosz, Paweł Verfasser (DE-588)1069425044 aut Data mining algorithms explained using R Paweł Cichosz 1. publ. Chichester Wiley 2015 XXXI, 683 S. Ill., graph. Darst. 252 mm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index R Programm (DE-588)4705956-4 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s R Programm (DE-588)4705956-4 s DE-604 Erscheint auch als Online-Ausgabe 978-1-118-95080-7 Erscheint auch als Online-Ausgabe 978-1-118-95084-5 http://catalogimages.wiley.com/images/db/jimages/9781118332580.jpg Cover Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027877623&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cichosz, Paweł Data mining algorithms explained using R R Programm (DE-588)4705956-4 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4428654-5 |
| title | Data mining algorithms explained using R |
| title_auth | Data mining algorithms explained using R |
| title_exact_search | Data mining algorithms explained using R |
| title_full | Data mining algorithms explained using R Paweł Cichosz |
| title_fullStr | Data mining algorithms explained using R Paweł Cichosz |
| title_full_unstemmed | Data mining algorithms explained using R Paweł Cichosz |
| title_short | Data mining algorithms |
| title_sort | data mining algorithms explained using r |
| title_sub | explained using R |
| topic | R Programm (DE-588)4705956-4 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | R Programm Data Mining |
| url | http://catalogimages.wiley.com/images/db/jimages/9781118332580.jpg http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027877623&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cichoszpaweł dataminingalgorithmsexplainedusingr |