Finding groups in data: an introduction to cluster analysis
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ [u.a.]
Wiley
2005
|
| Schriftenreihe: | Wiley series in probability and statistics
Wiley-Interscience paperback series |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 320 - 331 |
| Beschreibung: | XIV, 342 S. graph. Darst. |
| ISBN: | 0471735787 |
Internformat
MARC
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| 035 | |a (OCoLC)57527674 | ||
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| 100 | 1 | |a Kaufman, Leonard |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Finding groups in data |b an introduction to cluster analysis |c Leonard Kaufman ; Peter J. Rousseeuw |
| 264 | 1 | |a Hoboken, NJ [u.a.] |b Wiley |c 2005 | |
| 300 | |a XIV, 342 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series in probability and statistics | |
| 490 | 0 | |a Wiley-Interscience paperback series | |
| 500 | |a Literaturverz. S. 320 - 331 | ||
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| 700 | 1 | |a Rousseeuw, Peter J. |e Verfasser |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804135133324771328 |
|---|---|
| adam_text | Contents
1.
Introduction
І
1.
Motivation,
1
2.
Types of Data and How to Handle Them,
3
2.1
Interval-Scaled Variables,
4
2.2
Dissimilarities,
16
2.3
Similarities,
20
2.4
Binary Variables,
22
2.5
Nominal, Ordinal, and Ratio Variables,
28
2.6
Mixed Variables,
32
3.
Which Clustering Algorithm to Choose,
37
3.1
Partitioning Methods,
38
3.2
Hierarchical Methods,
44
4.
A Schematic Overview of Our Programs,
50
5.
Computing Dissimilarities with the Program DAISY,
52
Exercises and Problems,
63
2.
Partitioning Around Medoids (Program
РАМ)
68
1.
Short Description of the Method,
68
2.
How to Use the Program
РАМ,
72
2.1
Interactive Use and Input, V
2.2
Output,
80
2.3
Missing Values,
88
3.
Examples,
92
*4. More on the Algorithm and the Program,
102
4.1
Description of the Algorithm,
102
4.2
Structure of the Program,
104
xi
CONTENTS
*5.
Related Methods and References,
108
5.1
The
À>Medoid
Method and Optimal Plant Location,
108
5.2
Other Methods Based on the Selection of Representative
Objects,
110
5.3
Methods Based on the Construction of Central Points, 111
5.4
Some Other Nonhierarchical Methods,
116
5.5
Why Did We Choose the ¿-Medoid Method?,
117
5.6
Graphical Displays,
119
Exercises and Problems,
123
3.
Clustering Large Applications (Program CLARA)
126
1.
Short Description of the Method,
126
2.
How to Use the Program CLARA,
127
2.1
Interactive Use and Input,
127
2.2
Output,
130
2.3
Missing Values,
134
3.
An Example,
139
*4. More on the Algorithm and the Program,
144
4.1
Description of the Algorithm,
144
4.2
Structure of the Program,
146
4.3
Limitations and Special Messages,
151
4.4
Modifications and Extensions of CLARA,
153
*5. Related Methods and References,
155
5.1
Partitioning Methods for Large Data Sets,
155
5.2
Hierarchical Methods for Large Data Sets,
157
5.3
Implementing CLARA on a Parallel Computer,
160
Exercises and Problems,
162
4.
Fuzzy Analysis (Program FANNY)
164
1.
The Purpose of Fuzzy Clustering,
164
2.
How to Use the Program FANNY,
166
2.1
Interactive Use and Input,
167
2.2
Output,
170
3.
Examples,
175
*4. More on the Algorithm and the Program,
182
4.1
Description of the Algorithm,
182
4.2
Structure of the Program,
188
CONTENTS
X»i
*5.
Related Methods and References,
189
5.1
Fuzzy ¿-Means and the MND2 Method,
189
5.2
Why Did We Choose FANNY?,
191
5.3
Measuring the Amount of Fuzziness,
191
5.4
A Graphical Display of Fuzzy Memberships,
195
Exercises and Problems,
197
5.
Agglomerative Nesting (Program AGNES)
199
1.
Short Description of the Method,
199
2.
How to Use the Program AGNES,
208
2Л
Interactive Use and Input,
208
2.2
Output,
209
3.
Examples,
214
*4. More on the Algorithm and the Program,
221
4.1
Description of the Algorithm,
221
4.2
Structure of the Program,
223
*5. Related Methods and References,
224
5.1
Other Agglomerative Clustering Methods,
224
5.2
Comparing Their Properties,
238
5.3
Graphical Displays,
243
Exercises and Problems,
250
6.
Divisive Analysis (Program DIANA)
253
1.
Short Description of the Method,
253
2.
How to Use the Program DIANA,
259
3.
Examples,
263
*4. More on the Algorithm and the Program,
271
4.1
Description of the Algorithm,
271
4.2
Structure of the Program,
272
*5. Related Methods and References,
273
5.1
Variants of the Selected Method,
273
5.2
Other Divisive Techniques,
7,15
Exercises and Problems,
277
7.
Monothetic Analysis (Program
MONA)
1.
Short Description of the Method,
280
2,
How to Use the Program
MONA, 283
XJV CONTENTS
2.1
Interactive Use and Input,
284
2.2
Output,
287
3.
Examples,
290
*4. More on the Algorithm and the Program,
298
4.1
Description of the Algorithm,
298
4.2
Structure of the Program,
301
*5. Related Methods and References,
304
5.1
Association Analysis,
304
5.2
Other Monothetic Divisive Algorithms
for Binary Data,
307
5.3
Some Other Divisive Clustering Methods,
308
Exercises and Problems,
310
APPENDIX
312
1.
Implementation and Structure of the Programs,
312
2.
Running the Programs,
313
3.
Adapting the Programs to Your Needs,
316
4.
The Program CLUSPLOT,
318
References
320
Author Index
332
Subject Index
335
|
| adam_txt |
Contents
1.
Introduction
І
1.
Motivation,
1
2.
Types of Data and How to Handle Them,
3
2.1
Interval-Scaled Variables,
4
2.2
Dissimilarities,
16
2.3
Similarities,
20
2.4
Binary Variables,
22
2.5
Nominal, Ordinal, and Ratio Variables,
28
2.6
Mixed Variables,
32
3.
Which Clustering Algorithm to Choose,
37
3.1
Partitioning Methods,
38
3.2
Hierarchical Methods,
44
4.
A Schematic Overview of Our Programs,
50
5.
Computing Dissimilarities with the Program DAISY,
52
Exercises and Problems,
63
2.
Partitioning Around Medoids (Program
РАМ)
68
1.
Short Description of the Method,
68
2.
How to Use the Program
РАМ,
72
2.1
Interactive Use and Input, 'V
2.2
Output,
80
2.3
Missing Values,
88
3.
Examples,
92
*4. More on the Algorithm and the Program,
102
4.1
Description of the Algorithm,
102
4.2
Structure of the Program,
104
xi
CONTENTS
*5.
Related Methods and References,
108
5.1
The
À>Medoid
Method and Optimal Plant Location,
108
5.2
Other Methods Based on the Selection of Representative
Objects,
110
5.3
Methods Based on the Construction of Central Points, 111
5.4
Some Other Nonhierarchical Methods,
116
5.5
Why Did We Choose the ¿-Medoid Method?,
117
5.6
Graphical Displays,
119
Exercises and Problems,
123
3.
Clustering Large Applications (Program CLARA)
126
1.
Short Description of the Method,
126
2.
How to Use the Program CLARA,
127
2.1
Interactive Use and Input,
127
2.2
Output,
130
2.3
Missing Values,
134
3.
An Example,
139
*4. More on the Algorithm and the Program,
144
4.1
Description of the Algorithm,
144
4.2
Structure of the Program,
146
4.3
Limitations and Special Messages,
151
4.4
Modifications and Extensions of CLARA,
153
*5. Related Methods and References,
155
5.1
Partitioning Methods for Large Data Sets,
155
5.2
Hierarchical Methods for Large Data Sets,
157
5.3
Implementing CLARA on a Parallel Computer,
160
Exercises and Problems,
162
4.
Fuzzy Analysis (Program FANNY)
164
1.
The Purpose of Fuzzy Clustering,
164
2.
How to Use the Program FANNY,
166
2.1
Interactive Use and Input,
167
2.2
Output,
170
3.
Examples,
175
*4. More on the Algorithm and the Program,
182
4.1
Description of the Algorithm,
182
4.2
Structure of the Program,
188
CONTENTS
X»i
*5.
Related Methods and References,
189
5.1
Fuzzy ¿-Means and the MND2 Method,
189
5.2
Why Did We Choose FANNY?,
191
5.3
Measuring the Amount of Fuzziness,
191
5.4
A Graphical Display of Fuzzy Memberships,
195
Exercises and Problems,
197
5.
Agglomerative Nesting (Program AGNES)
199
1.
Short Description of the Method,
199
2.
How to Use the Program AGNES,
208
2Л
Interactive Use and Input,
208
2.2
Output,
209
3.
Examples,
214
*4. More on the Algorithm and the Program,
221
4.1
Description of the Algorithm,
221
4.2
Structure of the Program,
223
*5. Related Methods and References,
224
5.1
Other Agglomerative Clustering Methods,
224
5.2
Comparing Their Properties,
238
5.3
Graphical Displays,
243
Exercises and Problems,
250
6.
Divisive Analysis (Program DIANA)
253
1.
Short Description of the Method,
253
2.
How to Use the Program DIANA,
259
3.
Examples,
263
*4. More on the Algorithm and the Program,
271
4.1
Description of the Algorithm,
271
4.2
Structure of the Program,
272
*5. Related Methods and References,
273
5.1
Variants of the Selected Method,
273
5.2
Other Divisive Techniques,
7,15
Exercises and Problems,
277
7.
Monothetic Analysis (Program
MONA)
1.
Short Description of the Method,
280
2,
How to Use the Program
MONA, 283
XJV CONTENTS
2.1
Interactive Use and Input,
284
2.2
Output,
287
3.
Examples,
290
*4. More on the Algorithm and the Program,
298
4.1
Description of the Algorithm,
298
4.2
Structure of the Program,
301
*5. Related Methods and References,
304
5.1
Association Analysis,
304
5.2
Other Monothetic Divisive Algorithms
for Binary Data,
307
5.3
Some Other Divisive Clustering Methods,
308
Exercises and Problems,
310
APPENDIX
312
1.
Implementation and Structure of the Programs,
312
2.
Running the Programs,
313
3.
Adapting the Programs to Your Needs,
316
4.
The Program CLUSPLOT,
318
References
320
Author Index
332
Subject Index
335 |
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| id | DE-604.BV021323477 |
| illustrated | Illustrated |
| index_date | 2024-07-02T13:59:36Z |
| indexdate | 2024-07-09T20:35:39Z |
| institution | BVB |
| isbn | 0471735787 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014643843 |
| oclc_num | 57527674 |
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| physical | XIV, 342 S. graph. Darst. |
| publishDate | 2005 |
| publishDateSearch | 2005 |
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| publisher | Wiley |
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| series2 | Wiley series in probability and statistics Wiley-Interscience paperback series |
| spelling | Kaufman, Leonard Verfasser aut Finding groups in data an introduction to cluster analysis Leonard Kaufman ; Peter J. Rousseeuw Hoboken, NJ [u.a.] Wiley 2005 XIV, 342 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Wiley-Interscience paperback series Literaturverz. S. 320 - 331 Clusteranalyse gtt Cluster analysis Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 s DE-604 Rousseeuw, Peter J. Verfasser aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014643843&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kaufman, Leonard Rousseeuw, Peter J. Finding groups in data an introduction to cluster analysis Clusteranalyse gtt Cluster analysis Cluster-Analyse (DE-588)4070044-6 gnd |
| subject_GND | (DE-588)4070044-6 |
| title | Finding groups in data an introduction to cluster analysis |
| title_auth | Finding groups in data an introduction to cluster analysis |
| title_exact_search | Finding groups in data an introduction to cluster analysis |
| title_exact_search_txtP | Finding groups in data an introduction to cluster analysis |
| title_full | Finding groups in data an introduction to cluster analysis Leonard Kaufman ; Peter J. Rousseeuw |
| title_fullStr | Finding groups in data an introduction to cluster analysis Leonard Kaufman ; Peter J. Rousseeuw |
| title_full_unstemmed | Finding groups in data an introduction to cluster analysis Leonard Kaufman ; Peter J. Rousseeuw |
| title_short | Finding groups in data |
| title_sort | finding groups in data an introduction to cluster analysis |
| title_sub | an introduction to cluster analysis |
| topic | Clusteranalyse gtt Cluster analysis Cluster-Analyse (DE-588)4070044-6 gnd |
| topic_facet | Clusteranalyse Cluster analysis Cluster-Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014643843&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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