Description

Book Synopsis

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, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.

The book focuses on three primary aspects of data clustering:

  • Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization
  • Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biologic

    Table of Contents

    An Introduction to Cluster Analysis. Feature Selection for Clustering: A Review. Probabilistic Models for Clustering. A Survey of Partitional and Hierarchical Clustering Algorithms. Density-Based Clustering. Grid-Based Clustering. Non-Negative Matrix Factorizations for Clustering: A Survey. Spectral Clustering. Clustering High-Dimensional Data. A Survey of Stream Clustering Algorithms. Big Data Clustering. Clustering Categorical Data. Document Clustering: The Next Frontier. Clustering Multimedia Data. Time Series Data Clustering. Clustering Biological Data. Network Clustering. A Survey of Uncertain Data Clustering Algorithms. Concepts of Visual and Interactive Clustering. Semi-Supervised Clustering. Alternative Clustering Analysis: A Review. Cluster Ensembles: Theory and Applications. Clustering Validation Measures. Educational and Software Resources for Data Clustering. Index.

Data Clustering

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    £114.00

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    RRP £120.00 – you save £6.00 (5%)

    Order before 4pm today for delivery by Mon 8 Jun 2026.

    A Hardback by Charu C. Aggarwal, Chandan K. Reddy

    1 in stock


      View other formats and editions of Data Clustering by Charu C. Aggarwal

      Publisher: Taylor & Francis Inc
      Publication Date: 1/21/2013 12:08:00 AM
      ISBN13: 9781466558212, 978-1466558212
      ISBN10: 1466558210
      Also in:
      Computing Data capture and analysis Technology, Engineering & Agriculture Automatic control engineering

      Description

      Book Synopsis

      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, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.

      The book focuses on three primary aspects of data clustering:

      • Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization
      • Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biologic

        Table of Contents

        An Introduction to Cluster Analysis. Feature Selection for Clustering: A Review. Probabilistic Models for Clustering. A Survey of Partitional and Hierarchical Clustering Algorithms. Density-Based Clustering. Grid-Based Clustering. Non-Negative Matrix Factorizations for Clustering: A Survey. Spectral Clustering. Clustering High-Dimensional Data. A Survey of Stream Clustering Algorithms. Big Data Clustering. Clustering Categorical Data. Document Clustering: The Next Frontier. Clustering Multimedia Data. Time Series Data Clustering. Clustering Biological Data. Network Clustering. A Survey of Uncertain Data Clustering Algorithms. Concepts of Visual and Interactive Clustering. Semi-Supervised Clustering. Alternative Clustering Analysis: A Review. Cluster Ensembles: Theory and Applications. Clustering Validation Measures. Educational and Software Resources for Data Clustering. Index.

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