Data Clustering : Algorithms and Applications.

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Aggarwal, Charu C.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken : CRC Press, 2013.
Colección:Chapman & Hall/CRC data mining and knowledge discovery series.
Temas:
Document clustering.
Cluster analysis.
Data mining.
Machine theory.
File organization (Computer science)
Data Mining
Cluster Analysis
Regroupement des documents (Informatique)
Classification automatique (Statistique)
Exploration de données (Informatique)
Théorie des automates.
Fichiers (Informatique) > Organisation.
COMPUTERS > General.
MATHEMATICS > Applied.
MATHEMATICS > Probability & Statistics > General.
Cluster analysis
Data mining
Document clustering
Machine theory
Acceso en línea:Texto completo
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.

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