Partitional Clustering via Nonsmooth Optimization
Douban
Clustering via Optimization
Adil M. Bagirov / Napsu Karmitsa …
sinopsis
This 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 big data. 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.
contenidos
Introduction to Clustering Pages 3-13
M. Bagirov, Adil (et al.)
Theory of Nonsmooth Optimization Pages 15-50
M. Bagirov, Adil (et al.)
Nonsmooth Optimization Methods Pages 51-94
M. Bagirov, Adil (et al.)
Optimization Models in Cluster Analysis Pages 97-133
M. Bagirov, Adil (et al.)
Heuristic Clustering Algorithms Pages 135-163
M. Bagirov, Adil (et al.)
Metaheuristic Clustering Algorithms Pages 165-183
M. Bagirov, Adil (et al.)
Incremental Clustering Algorithms Pages 185-200
M. Bagirov, Adil (et al.)
Nonsmooth Optimization Based Clustering Algorithms Pages 201-223
M. Bagirov, Adil (et al.)
DC Optimization Based Clustering Algorithms Pages 225-241
M. Bagirov, Adil (et al.)
Performance and Evaluation Measures Pages 245-268
M. Bagirov, Adil (et al.)
Implementations and Data Sets Pages 269-279
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Numerical Experiments Pages 281-314
M. Bagirov, Adil (et al.)
Concluding Remarks Pages 315-317
M. Bagirov, Adil (et al.)