Mathematical Classification and Clustering - Couverture souple

Mirkin, Boris

Couverture souple

ISBN 10 : 146138057X ISBN 13 : 9781461380573

Editeur : Springer, 2011

Synopsis

I am very happy to have this opportunity to present the work of Boris Mirkin, a distinguished Russian scholar in the areas of data analysis and decision making methodologies. The monograph is devoted entirely to clustering, a discipline dispersed through many theoretical and application areas, from mathematical statistics and combina- torial optimization to biology, sociology and organizational structures. It compiles an immense amount of research done to date, including many original Russian de- velopments never presented to the international community before (for instance, cluster-by-cluster versions of the K-Means method in Chapter 4 or uniform par- titioning in Chapter 5). The author's approach, approximation clustering, allows him both to systematize a great part of the discipline and to develop many in- novative methods in the framework of optimization problems. The optimization methods considered are proved to be meaningful in the contexts of data analysis and clustering. The material presented in this book is quite interesting and stimulating in paradigms, clustering and optimization. On the other hand, it has a substantial application appeal. The book will be useful both to specialists and students in the fields of data analysis and clustering as well as in biology, psychology, economics, marketing research, artificial intelligence, and other scientific disciplines. Panos Pardalos, Series Editor.

A propos du livre

Présentation de l'éditeur

The goal of the book is threefold; first, to serve as a reference for the enormous amount of existing clustering concepts and methods; second, to be used as a textbook; and, third, to present the author's and his Russian colleagues' results in the perspective of current developments. It contains several unique features: It is the only book to contain an up-to-date review of clustering including the most recent theories about discrete clustering structures (subsets, partitions, hierarchies etc.) in their relation to data. An approximation framework is developed as a major construct substantiating and extending such existing approaches as agglomerative clustering and K-means method, and leading to new methods such as box and ideal type clustering, uniform partitioning, aggregation of flow tables, and principal cluster analysis: The opening chapter is devoted to a review of classification and clustering goals and forms prior to defining the scope and goals of clustering. A dozen real-world illustrative examples are interwoven throughout the exposition. £/LIST£ Audience: The book will be useful to both specialists and students in the field of data analysis and clustering as well as in biology, psychology, economics, marketing research, artificial intelligence, and other scientific disciplines.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2011
• Langue: anglais
• ISBN 10: 146138057X
• ISBN 13: 9781461380573
• Reliure: Broché
• Nombre de pages: 448
• Coordonnées du fabricant: non disponible