9780470276808 - Clustering (IEEE Press Series on Computational Intelligence) von Xu, Rui; Wunsch, Don (7 Ergebnisse)

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In.

Zustand: New. pp. x + 358 Illus.

Gebunden. Zustand: New. Rui Xu, PhD, is a Research Associate in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research interests include computational intelligence, machine learning, data mining, neural networks, patter.

Hardcover. Zustand: Brand New. illustrated edition. 358 pages. 9.25x6.25x1.00 inches. In Stock.

Zustand: New. Written by two of the best-known experts in the field, Clustering is the only thoroughly comprehensive text on the subject currently available. The book looks at the full range of clustering and provides enough detail to allow users to select the method that best fits their application. Series: IEEE Press Series on Computational Intelligence. Num Pages: 368 pages, Illustrations. BIC Classification: PB; TJ; UYQE. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 236 x 163 x 23. Weight in Grams: 640. . 2008. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - The only thorough, comprehensive book available on clusteringFrom two of the best-known experts in the field comes the first book to take a truly comprehensive look at clustering. The book begins with a complete introduction to cluster analysis in which readers will become familiarized with classification and clustering; definition of clusters; clustering applications; and the literature of clustering algorithms. The authors then present a detailed outline of the book's content and go on to explore:\* Proximity measures\* Hierarchical clustering\* Partition clustering\* Neural network-based clustering\* Kernel-based clustering\* Sequential data clustering\* Large-scale data clustering\* Data visualization and high-dimensional data clustering\* Cluster validationThe authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds. The book is intended as a professional reference for computer scientists and applied mathematicians working with data-intensive applications, and for computational intelligence researchers who use clustering for feature selection or data reduction. Its selection of homework exercises also makes it appropriate as a textbook for graduate students in mathematics, science, and engineering.