Partitional Clustering via Nonsmooth Optimization

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.

• Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar;
• Puts theory in context, so readers gain knowledge about the most essential concepts and algorithms;
• Covers essential terms, algorithms, and useful tools for learning and performing contemporary AI.

Autor*in

Adil Bagirov

Themen in »Partitional Clustering via Nonsmooth Optimization«

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

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