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Home > Technology > Electronics > Advances in Fuzzy Clustering and its Applications-eBook
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Advances in Fuzzy Clustering and its Applications
 
 
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A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering.

Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book addresses timely and relevant concepts and methods, whilst identifying major challenges and recent developments in the area. Split into five clear sections, Fundamentals, Visualization, Algorithms and Computational Aspects, Real-Time and Dynamic Clustering, and Applications and Case Studies, the book covers a wealth of novel, original and fully updated material, and in particular offers:

  • a focus on the algorithmic and computational augmentations of fuzzy clustering and its effectiveness in handling high dimensional problems, distributed problem solving and uncertainty management.
  • presentations of the important and relevant phases of cluster design, including the role of information granules, fuzzy sets in the realization of human-centricity facet of data analysis, as well as system modelling
  • demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects
  • a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role

This book will be of key interest to engineers associated with fuzzy control, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering disciplines, will find this an invaluable resource and research tool.

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Title of ebook: Advances in Fuzzy Clustering and its Applications
ISBN: 9780470061183
Publisher: John Wiley & Sons, Inc.
Internet download file size: 8777 kb
Pages: 454
Released online for download: 06-13-2007
Editor: de Oliveira, Jose Valente
Editor: Pedrycz, Witold


Advances in Fuzzy Clustering and its Applications



Chapter One

Fundamentals of Fuzzy Clustering

Rudolf Kruse, Christian Döring, and Marie-Jeanne Lesot

Department of Knowledge Processing and Language Engineering, University of Magdeburg, Germany

1.1 INTRODUCTION

Clustering is an unsupervised learning task that aims at decomposing a given set of objects into subgroups or clusters based on similarity. The goal is to divide the data-set in such a way that objects (or example cases) belonging to the same cluster are as similar as possible, whereas objects belonging to different clusters are as dissimilar as possible. The motivation for finding and building classes in this way can be manifold (Bock, 1974). Cluster analysis is primarily a tool for discovering previously hidden structure in a set of unordered objects. In this case one assumes that a `true' ... read full excerpt from Advances in Fuzzy Clustering and its Applications ebook





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