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Springer Algorithms for Fuzzy Clustering - c-Means Methods

62 black & white illustrations, 23 black

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Description

Explore the essential principles of cluster analysis with this specialized text from Springer. As researchers increasingly rely on cluster analysis for exploratory data analysis and data mining, understanding the mathematical frameworks behind these tools becomes vital. This volume, part of the Studies in Fuzziness and Soft Computing series, focuses on the methods used in c-Means clustering. Clustering algorithms offer significant flexibility, allowing users to select specific mathematical frameworks based on their unique application needs. This book addresses the diverse range of outputs available in the field, from traditional dendrograms used in agglomerative clustering to modern self-organizing maps. Whether you are working in different scientific fields or specializing in computer science, this resource provides the technical foundation required to navigate complex data sets through effective clustering techniques.

Key Features

Focuses on c-Means clustering methods to support advanced research in data mining and exploratory data analysis.

Provides insight into various mathematical frameworks that allow for flexible selection based on specific application requirements.

Covers a wide range of clustering outputs including agglomerative clustering dendrograms and self-organizing maps.

Part of the Studies in Fuzziness and Soft Computing series for specialized academic and professional study.

Designed for specialists across different scientific fields who utilize clustering as a primary tool for data analysis.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
15 April 2008
Listed Since
05 March 2008

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