Clustering:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Oxford
Wiley
c2009
|
| Schriftenreihe: | IEEE series on computational intelligence
|
| Schlagworte: | |
| Online-Zugang: | FHI01 FHN01 Volltext |
| Beschreibung: | Includes bibliographical references and index COVER -- CONTENTS -- PREFACE -- 1. CLUSTER ANALYSIS -- 1.1. Classification and Clustering -- 1.2. Definition of Clusters -- 1.3. Clustering Applications -- 1.4. Literature of Clustering Algorithms -- 1.5. Outline of the Book -- 2. PROXIMITY MEASURES -- 2.1. Introduction -- 2.2. Feature Types and Measurement Levels -- 2.3. Definition of Proximity Measures -- 2.4. Proximity Measures for Continuous Variables -- 2.5. Proximity Measures for Discrete Variables -- 2.6. Proximity Measures for Mixed Variables -- 2.7. Summary -- 3. HIERARCHICAL CLUSTERING -- 3.1. Introduction -- 3.2. Agglomerative Hierarchical Clustering -- 3.3. Divisive Hierarchical Clustering -- 3.4. Recent Advances -- 3.5. Applications -- 3.6. Summary -- 4. PARTITIONAL CLUSTERING -- 4.1. Introduction -- 4.2. Clustering Criteria -- 4.3. K-Means Algorithm -- 4.4. Mixture Density-Based Clustering -- 4.5. Graph Theory-Based Clustering -- 4.6. Fuzzy Clustering -- 4.7. Search Techniques-Based Clustering Algorithms -- - 4.8. Applications -- 4.9. Summary -- 5. NEURAL NETWORK-BASED CLUSTERING -- 5.1. Introduction -- 5.2. Hard Competitive Learning Clustering -- 5.3. Soft Competitive Learning Clustering -- 5.4. Applications -- 5.5. Summary -- 6. KERNEL-BASED CLUSTERING -- 6.1. Introduction -- 6.2. Kernel Principal Component Analysis -- 6.3. Squared-Error-Based Clustering with Kernel Functions -- 6.4. Support Vector Clustering -- 6.5. Applications -- 6.6. Summary -- 7. SEQUENTIAL DATA CLUSTERING -- 7.1. Introduction -- 7.2. Sequence Similarity -- 7.3. Indirect Sequence Clustering -- 7.4. Model-Based Sequence Clustering -- 7.5. Applications-Genomic and Biological Sequence Clustering -- 7.6. Summary -- 8. LARGE-SCALE DATA CLUSTERING -- 8.1. Introduction -- 8.2. Random Sampling Methods -- 8.3. Condensation-Based Methods -- 8.4. Density-Based Methods -- 8.5. Grid-Based Methods -- 8.6. Divide and Conquer -- 8.7. Incremental Clustering -- 8.8. Applications -- 8.9. Summary -- - 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING -- 9.1. Introduction -- 9.2. Linear Projection Algorithms -- 9.3. Nonlinear Projection Algorithms -- 9.4. Projected and Subspace Clustering -- 9.5. Applications -- 9.6. Summary -- 10. CLUSTER VALIDITY -- 10.1. Introduction -- 10.2. External Criteria -- 10.3. Internal Criteria -- 10.4. Relative Criteria -- 10.5. Summary -- 11. CONCLUDING REMARKS -- PROBLEMS -- REFERENCES -- AUTHOR INDEX -- SUBJECT INDEX. |
| Beschreibung: | 1 Online-Ressource (x, 358 S.) |
| ISBN: | 9780470382776 0470382775 9780470382783 0470382783 9780470276808 0470276800 |
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| 500 | |a COVER -- CONTENTS -- PREFACE -- 1. CLUSTER ANALYSIS -- 1.1. Classification and Clustering -- 1.2. Definition of Clusters -- 1.3. Clustering Applications -- 1.4. Literature of Clustering Algorithms -- 1.5. Outline of the Book -- 2. PROXIMITY MEASURES -- 2.1. Introduction -- 2.2. Feature Types and Measurement Levels -- 2.3. Definition of Proximity Measures -- 2.4. Proximity Measures for Continuous Variables -- 2.5. Proximity Measures for Discrete Variables -- 2.6. Proximity Measures for Mixed Variables -- 2.7. Summary -- 3. HIERARCHICAL CLUSTERING -- 3.1. Introduction -- 3.2. Agglomerative Hierarchical Clustering -- 3.3. Divisive Hierarchical Clustering -- 3.4. Recent Advances -- 3.5. Applications -- 3.6. Summary -- 4. PARTITIONAL CLUSTERING -- 4.1. Introduction -- 4.2. Clustering Criteria -- 4.3. K-Means Algorithm -- 4.4. Mixture Density-Based Clustering -- 4.5. Graph Theory-Based Clustering -- 4.6. Fuzzy Clustering -- 4.7. Search Techniques-Based Clustering Algorithms -- | ||
| 500 | |a - 4.8. Applications -- 4.9. Summary -- 5. NEURAL NETWORK-BASED CLUSTERING -- 5.1. Introduction -- 5.2. Hard Competitive Learning Clustering -- 5.3. Soft Competitive Learning Clustering -- 5.4. Applications -- 5.5. Summary -- 6. KERNEL-BASED CLUSTERING -- 6.1. Introduction -- 6.2. Kernel Principal Component Analysis -- 6.3. Squared-Error-Based Clustering with Kernel Functions -- 6.4. Support Vector Clustering -- 6.5. Applications -- 6.6. Summary -- 7. SEQUENTIAL DATA CLUSTERING -- 7.1. Introduction -- 7.2. Sequence Similarity -- 7.3. Indirect Sequence Clustering -- 7.4. Model-Based Sequence Clustering -- 7.5. Applications-Genomic and Biological Sequence Clustering -- 7.6. Summary -- 8. LARGE-SCALE DATA CLUSTERING -- 8.1. Introduction -- 8.2. Random Sampling Methods -- 8.3. Condensation-Based Methods -- 8.4. Density-Based Methods -- 8.5. Grid-Based Methods -- 8.6. Divide and Conquer -- 8.7. Incremental Clustering -- 8.8. Applications -- 8.9. Summary -- | ||
| 500 | |a - 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING -- 9.1. Introduction -- 9.2. Linear Projection Algorithms -- 9.3. Nonlinear Projection Algorithms -- 9.4. Projected and Subspace Clustering -- 9.5. Applications -- 9.6. Summary -- 10. CLUSTER VALIDITY -- 10.1. Introduction -- 10.2. External Criteria -- 10.3. Internal Criteria -- 10.4. Relative Criteria -- 10.5. Summary -- 11. CONCLUDING REMARKS -- PROBLEMS -- REFERENCES -- AUTHOR INDEX -- SUBJECT INDEX. | ||
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Datensatz im Suchindex
| _version_ | 1804150098620317696 |
|---|---|
| any_adam_object | |
| author | Xu, Rui Wunsch, Donald C. |
| author_facet | Xu, Rui Wunsch, Donald C. |
| author_role | aut aut |
| author_sort | Xu, Rui |
| author_variant | r x rx d c w dc dcw |
| building | Verbundindex |
| bvnumber | BV040768928 |
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| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV040768928 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:33:31Z |
| institution | BVB |
| isbn | 9780470382776 0470382775 9780470382783 0470382783 9780470276808 0470276800 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025747346 |
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| spelling | Xu, Rui Verfasser aut Clustering by Rui Xu, Donald C. Wunsch Oxford Wiley c2009 1 Online-Ressource (x, 358 S.) txt rdacontent c rdamedia cr rdacarrier IEEE series on computational intelligence Includes bibliographical references and index COVER -- CONTENTS -- PREFACE -- 1. CLUSTER ANALYSIS -- 1.1. Classification and Clustering -- 1.2. Definition of Clusters -- 1.3. Clustering Applications -- 1.4. Literature of Clustering Algorithms -- 1.5. Outline of the Book -- 2. PROXIMITY MEASURES -- 2.1. Introduction -- 2.2. Feature Types and Measurement Levels -- 2.3. Definition of Proximity Measures -- 2.4. Proximity Measures for Continuous Variables -- 2.5. Proximity Measures for Discrete Variables -- 2.6. Proximity Measures for Mixed Variables -- 2.7. Summary -- 3. HIERARCHICAL CLUSTERING -- 3.1. Introduction -- 3.2. Agglomerative Hierarchical Clustering -- 3.3. Divisive Hierarchical Clustering -- 3.4. Recent Advances -- 3.5. Applications -- 3.6. Summary -- 4. PARTITIONAL CLUSTERING -- 4.1. Introduction -- 4.2. Clustering Criteria -- 4.3. K-Means Algorithm -- 4.4. Mixture Density-Based Clustering -- 4.5. Graph Theory-Based Clustering -- 4.6. Fuzzy Clustering -- 4.7. Search Techniques-Based Clustering Algorithms -- - 4.8. Applications -- 4.9. Summary -- 5. NEURAL NETWORK-BASED CLUSTERING -- 5.1. Introduction -- 5.2. Hard Competitive Learning Clustering -- 5.3. Soft Competitive Learning Clustering -- 5.4. Applications -- 5.5. Summary -- 6. KERNEL-BASED CLUSTERING -- 6.1. Introduction -- 6.2. Kernel Principal Component Analysis -- 6.3. Squared-Error-Based Clustering with Kernel Functions -- 6.4. Support Vector Clustering -- 6.5. Applications -- 6.6. Summary -- 7. SEQUENTIAL DATA CLUSTERING -- 7.1. Introduction -- 7.2. Sequence Similarity -- 7.3. Indirect Sequence Clustering -- 7.4. Model-Based Sequence Clustering -- 7.5. Applications-Genomic and Biological Sequence Clustering -- 7.6. Summary -- 8. LARGE-SCALE DATA CLUSTERING -- 8.1. Introduction -- 8.2. Random Sampling Methods -- 8.3. Condensation-Based Methods -- 8.4. Density-Based Methods -- 8.5. Grid-Based Methods -- 8.6. Divide and Conquer -- 8.7. Incremental Clustering -- 8.8. Applications -- 8.9. Summary -- - 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING -- 9.1. Introduction -- 9.2. Linear Projection Algorithms -- 9.3. Nonlinear Projection Algorithms -- 9.4. Projected and Subspace Clustering -- 9.5. Applications -- 9.6. Summary -- 10. CLUSTER VALIDITY -- 10.1. Introduction -- 10.2. External Criteria -- 10.3. Internal Criteria -- 10.4. Relative Criteria -- 10.5. Summary -- 11. CONCLUDING REMARKS -- PROBLEMS -- REFERENCES -- AUTHOR INDEX -- SUBJECT INDEX. MATHEMATICS / Probability & Statistics / General bisacsh Cluster analysis MATHEMATICS / Probability & Statistics / General / bisacsh Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Electronic books Cluster-Analyse (DE-588)4070044-6 s DE-604 Wunsch, Donald C. Verfasser aut https://onlinelibrary.wiley.com/doi/book/10.1002/9780470382776 Verlag Volltext |
| spellingShingle | Xu, Rui Wunsch, Donald C. Clustering MATHEMATICS / Probability & Statistics / General bisacsh Cluster analysis MATHEMATICS / Probability & Statistics / General / bisacsh Cluster-Analyse (DE-588)4070044-6 gnd |
| subject_GND | (DE-588)4070044-6 |
| title | Clustering |
| title_auth | Clustering |
| title_exact_search | Clustering |
| title_full | Clustering by Rui Xu, Donald C. Wunsch |
| title_fullStr | Clustering by Rui Xu, Donald C. Wunsch |
| title_full_unstemmed | Clustering by Rui Xu, Donald C. Wunsch |
| title_short | Clustering |
| title_sort | clustering |
| topic | MATHEMATICS / Probability & Statistics / General bisacsh Cluster analysis MATHEMATICS / Probability & Statistics / General / bisacsh Cluster-Analyse (DE-588)4070044-6 gnd |
| topic_facet | MATHEMATICS / Probability & Statistics / General Cluster analysis MATHEMATICS / Probability & Statistics / General / bisacsh Cluster-Analyse |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9780470382776 |
| work_keys_str_mv | AT xurui clustering AT wunschdonaldc clustering |