Unsupervised and Semi-Supervised Learning: Clustering via Optimization

Bagirov, Adil; Karmitsa, Napsu; Taheri, Sona

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.

Details

Published by: Springer

Publication Date: 2024-12-17

Format: Hardcover

ISBN-13: 9783031765117

DOI: 10.1007/978-3-031-76512-4

Dimensions: 235cm x155cm

Pages: 395