Fuzzy Sets & their Application to Clustering & Training

Beatrice Lazzerini (Universita' di Pisa, Italy), Lakhmi C. Jain, D. Dumitrescu

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English

CRC Press Inc
24 March 2000

Set theory; Artificial intelligence

Series: International Series on Computational Intelligence

Fuzzy logic applications allow uncertain or imprecise data to be clustered and analyzed when traditional methods cannot be used. Very large and complex intelligence databases - such as those used in system design, banking and finance, medical diagnosis, and defense - are all candidates for real-world application of this cutting-edge technology. In this volume, an international team of authors provides a thorough introduction to fuzzy set theory then progresses through the algorithms and techniques used to manipulate data using fuzzy sets. These include classification, hierarchy, and cluster structure.

By: Beatrice Lazzerini (Universita' di Pisa Italy), Lakhmi C. Jain, D. Dumitrescu
Imprint: CRC Press Inc
Country of Publication: United States
Volume: 16
Dimensions: Height: 229mm, Width: 152mm, Spine: 39mm
Weight: 1.390kg
ISBN: 9780849305894
ISBN 10: 0849305896
Series: International Series on Computational Intelligence
Pages: 666
Publication Date: 24 March 2000
Audience: Professional and scholarly , Professional and scholarly , Professional & Vocational , Undergraduate , Further / Higher Education
Format: Hardback
Publisher's Status: Active

Fuzzy Sets. Entropy of Finite Fuzzy Partitions. Fuzziness and Non-Fuzziness Measures. Fuzzy Training Procedures. One-Level Classification: Cluster Substructure of a Fuzzy Class. One-Level Classification: Adaptive Algorithms. Cluster Validity. Convergence of Fuzzy clustering Algorithms. Fuzzy Discriminant Analysis and Related Clustering Criteria. Divisive Hierarchical Clustering. Classification with Structural Constraints. Classification in Pseudometric Spaces. Bibliography. NTI/Sales Copy

Beatrice Lazzerini, Lakhmi C. Jain, D. Dumitrescu