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Partitional Clustering via Nonsmooth Optimization

Clustering via Optimization

Napsu Karmitsa, Adil Bagirov, Sona Taheri, et al.

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ca. 149,79

Springer Nature Switzerland img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Elektronik, Elektrotechnik, Nachrichtentechnik

Beschreibung

This updated book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from very large data and data with noise and outliers. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.

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Schlagwörter

Heuristic clustering algorithms, Real-time clustering algorithms, Clustering with different similarity measures, Global optimization based clustering algorithms, Application of clustering algorithms, Optimization models of clustering problems, Clustering in very large data sets, Visualization of clustering results, Nonsmooth optimization based clustering algorithms, Clustering algorithms based on metaheuristics