Cluster and classification techniques for the biosciences:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2007
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Publisher description Table of contents only Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references (p. 224-239) and index |
| Physical Description: | XII, 246 S. Ill., graph. Darst. 26 cm |
| ISBN: | 0521852811 0521618002 9780521852814 9780521618007 |
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| 245 | 1 | 0 | |a Cluster and classification techniques for the biosciences |c Alan H. Fielding |
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| 650 | 4 | |a Cluster analysis | |
| 650 | 4 | |a Cluster Analysis | |
| 650 | 4 | |a Biometry |x methods | |
| 650 | 4 | |a Classification |x methods | |
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| 650 | 4 | |a Multivariate Analysis | |
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| adam_text | Contents
Preface page xi
1 Introduction 1
1.1 Background 1
1.2 Book structure 2
1.3 Classification 2
1.4 Clustering 3
1.5 Structures in data 3
1.6 Glossary 5
1.7 Recommended reading and other resources 10
2 Exploratory data analysis 12
2.1 Background 12
2.2 Dimensionality 13
2.3 Goodness of fit testing 14
2.4 Graphical methods 15
2.5 Variance based data projections 16
2.6 Distance based data projections 29
2.7 Other projection methods 32
2.8 Other methods 36
2.9 Data dredging 38
2.10 Example EDA analysis 38
3 Cluster analysis 46
3.1 Background 46
3.2 Distance and similarity measures 48
3.3 Partitioning methods 55
3.4 Agglomerative hierarchical methods 58
3.5 How many groups are there? 62
3.6 Divisive hierarchical methods 65
vii
vüi Contents
3.7 Two way clustering and gene shaving 66
3.8 Recommended reading 67
3.9 Example analyses 68
4 Introduction to classification 78
4.1 Background 78
4.2 Black box classifiers 81
4.3 Nature of a classifier 82
4.4 No free lunch 85
4.5 Bias and variance 86
4.6 Variable (feature) selection 87
4.7 Multiple classifiers 92
4.8 Why do classifiers fail? 94
4.9 Generalisation 95
4.10 Types of classifier 96
5 Classification algorithms 1 97
5.1 Background 97
5.2 Naive Bayes 99
5.3 Discriminant analysis 100
5.4 Logistic regression 117
5.5 Discriminant analysis or logistic regression? 128
5.6 Generalised additive modeis 130
5.7 Summary 136
6 Other classification methods 137
6.1 Background 137
6.2 Decision trees 137
6.3 Support vector machines 154
6.4 Artificial neural networks 156
6.5 Genetic algorithms 170
6.6 Others 175
6.7 Where next? 177
7 Classification accuracy 179
7.1 Background 179
7.2 Appropriate metrics 180
7.3 Binary accuracy measures 180
7.4 Appropriate testing data 183
7 5 Decision thresholds 186
7.6 Example 187
7.7 ROC plots 190
7.8 Incorporating costs 194
7.9 Comparing classifiers 196
7.10 Recommended reading 199
Contents ix
Appendix A 200
Appendix B 203
Appendix C 207
Appendix D 208
Appendix E 210
Appendix F 217
Appendix G 220
References 224
Index 241
|
| adam_txt |
Contents
Preface page xi
1 Introduction 1
1.1 Background 1
1.2 Book structure 2
1.3 Classification 2
1.4 Clustering 3
1.5 Structures in data 3
1.6 Glossary 5
1.7 Recommended reading and other resources 10
2 Exploratory data analysis 12
2.1 Background 12
2.2 Dimensionality 13
2.3 Goodness of fit testing 14
2.4 Graphical methods 15
2.5 Variance based data projections 16
2.6 Distance based data projections 29
2.7 Other projection methods 32
2.8 Other methods 36
2.9 Data dredging 38
2.10 Example EDA analysis 38
3 Cluster analysis 46
3.1 Background 46
3.2 Distance and similarity measures 48
3.3 Partitioning methods 55
3.4 Agglomerative hierarchical methods 58
3.5 How many groups are there? 62
3.6 Divisive hierarchical methods 65
vii
vüi Contents
3.7 Two way clustering and gene shaving 66
3.8 Recommended reading 67
3.9 Example analyses 68
4 Introduction to classification 78
4.1 Background 78
4.2 Black box classifiers 81
4.3 Nature of a classifier 82
4.4 No free lunch 85
4.5 Bias and variance 86
4.6 Variable (feature) selection 87
4.7 Multiple classifiers 92
4.8 Why do classifiers fail? 94
4.9 Generalisation 95
4.10 Types of classifier 96
5 Classification algorithms 1 97
5.1 Background 97
5.2 Naive Bayes 99
5.3 Discriminant analysis 100
5.4 Logistic regression 117
5.5 Discriminant analysis or logistic regression? 128
5.6 Generalised additive modeis 130
5.7 Summary 136
6 Other classification methods 137
6.1 Background 137
6.2 Decision trees 137
6.3 Support vector machines 154
6.4 Artificial neural networks 156
6.5 Genetic algorithms 170
6.6 Others 175
6.7 Where next? 177
7 Classification accuracy 179
7.1 Background 179
7.2 Appropriate metrics 180
7.3 Binary accuracy measures 180
7.4 Appropriate testing data 183
7 5 Decision thresholds 186
7.6 Example 187
7.7 ROC plots 190
7.8 Incorporating costs 194
7.9 Comparing classifiers 196
7.10 Recommended reading 199
Contents ix
Appendix A 200
Appendix B 203
Appendix C 207
Appendix D 208
Appendix E 210
Appendix F 217
Appendix G 220
References 224
Index 241 |
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| spelling | Fielding, Alan H. Verfasser aut Cluster and classification techniques for the biosciences Alan H. Fielding 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2007 XII, 246 S. Ill., graph. Darst. 26 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. 224-239) and index Biologie - Classification Biologie - Informatique Classification automatique (Statistique) Datenverarbeitung Biology Data processing Biology Classification Cluster analysis Cluster Analysis Biometry methods Classification methods Data Interpretation, Statistical Multivariate Analysis Biologie (DE-588)4006851-1 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 s Biologie (DE-588)4006851-1 s b DE-604 http://www.loc.gov/catdir/enhancements/fy0665/2006027983-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0665/2006027983-t.html Table of contents only HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015629240&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fielding, Alan H. Cluster and classification techniques for the biosciences Biologie - Classification Biologie - Informatique Classification automatique (Statistique) Datenverarbeitung Biology Data processing Biology Classification Cluster analysis Cluster Analysis Biometry methods Classification methods Data Interpretation, Statistical Multivariate Analysis Biologie (DE-588)4006851-1 gnd Cluster-Analyse (DE-588)4070044-6 gnd |
| subject_GND | (DE-588)4006851-1 (DE-588)4070044-6 |
| title | Cluster and classification techniques for the biosciences |
| title_auth | Cluster and classification techniques for the biosciences |
| title_exact_search | Cluster and classification techniques for the biosciences |
| title_exact_search_txtP | Cluster and classification techniques for the biosciences |
| title_full | Cluster and classification techniques for the biosciences Alan H. Fielding |
| title_fullStr | Cluster and classification techniques for the biosciences Alan H. Fielding |
| title_full_unstemmed | Cluster and classification techniques for the biosciences Alan H. Fielding |
| title_short | Cluster and classification techniques for the biosciences |
| title_sort | cluster and classification techniques for the biosciences |
| topic | Biologie - Classification Biologie - Informatique Classification automatique (Statistique) Datenverarbeitung Biology Data processing Biology Classification Cluster analysis Cluster Analysis Biometry methods Classification methods Data Interpretation, Statistical Multivariate Analysis Biologie (DE-588)4006851-1 gnd Cluster-Analyse (DE-588)4070044-6 gnd |
| topic_facet | Biologie - Classification Biologie - Informatique Classification automatique (Statistique) Datenverarbeitung Biology Data processing Biology Classification Cluster analysis Cluster Analysis Biometry methods Classification methods Data Interpretation, Statistical Multivariate Analysis Biologie Cluster-Analyse |
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