Data Clustering: Algorithms and Applications

Charu C. Aggarwal, Chandan K. Reddy

August 21, 2013 by Chapman and Hall/CRC
Reference - 652 Pages - 102 B/W Illustrations
ISBN 9781466558212 - CAT# K15510
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series


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  • Presents core methods for data clustering, including probabilistic, density- and grid-based, and spectral clustering
  • Explores various problems and scenarios pertaining to multimedia, text, biological, categorical, network, streams, and uncertain data
  • Offers in-depth insight on the clustering process, including different ways to cluster the same data set
  • Includes an extensive bibliography at the end of each chapter


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, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data
  • Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation

In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.


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