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By: (Edited by) Chandan K. Reddy , (Edited by) Charu C. Aggarwal
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In this book, top researchers from around the world cover the entire area of clustering, from basic methods to more refined and complex data clustering approaches. They pay special attention to recent issues in graphs, social networks, and other domains. The book explores the characteristics of clustering problems in a variety of application areas. It also explains how to glean detailed insight from the clustering processincluding how to verify the quality of the underlying clustersthrough supervision, human intervention, or the automated generation of alternative clusters.
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:
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 processincluding how to verify the quality of the underlying clustersthrough supervision, human intervention, or the automated generation of alternative clusters.
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