Data clustering algorithms and applications
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, fr...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press, Taylor & Francis Group
[2014]
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| Edición: | 1st edition |
| Colección: | Chapman & Hall/CRC data mining and knowledge discovery series.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628720106719 |
Tabla de Contenidos:
- Front Cover; Contents; Preface; Editor Biographies; Contributors; Chapter 1: An Introduction to Cluster Analysis; Chapter 2: Feature Selection for Clustering: A Review; Chapter 3: Probabilistic Models for Clustering; Chapter 4: A Survey of Partitional and Hierarchical Clustering Algorithms; Chapter 5: Density-Based Clustering; Chapter 6: Grid-Based Clustering; Chapter 7: Nonnegative Matrix Factorizations for Clustering: A Survey; Chapter 8: Spectral Clustering; Chapter 9: Clustering High-Dimensional Data; Chapter 10: A Survey of Stream Clustering Algorithms; Chapter 11: Big Data Clustering
- Chapter 12: Clustering Categorical DataChapter 13: Document Clustering: The Next Frontier; Chapter 14 : Clustering Multimedia Data; Chapter 15: Time-Series Data Clustering; Chapter 16: Clustering Biological Data; Chapter 17: Network Clustering; Chapter 18: A Survey of Uncertain Data Clustering Algorithms; Chapter 19: Concepts of Visual and Interactive Clustering; Chapter 20: Semisupervised Clustering; Chapter 21: Alternative Clustering Analysis: A Review; Chapter 22 : Cluster Ensembles: Theory and Applications; Chapter 23: Clustering ValidationMeasures
- Chapter 24: Educational and Software Resources for DataClusteringColor Inserts; Back Cover
