Fuzzy Sets & their Application to Clustering & Training
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A01=Beatrice Lazzerini
A01=D. Dumitrescu
A01=Lakhmi C. Jain
Ab A2
adaptive clustering techniques
advanced fuzzy clustering applications
algorithms
Archimedean T-norm
Atypical Points
Author_Beatrice Lazzerini
Author_D. Dumitrescu
Author_Lakhmi C. Jain
Category=PBCH
Category=UYQ
class
Class Ai
Cluster Substructure
CLUSTER VALIDITY
criterion
Data Set
degrees
discriminant analysis algorithms
entropy measures in classification
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
function
Fuzzy Class
Fuzzy Class Ai
Fuzzy Clustering
Fuzzy Clustering Algorithm
Fuzzy Hierarchy
Fuzzy Measure
Fuzzy Partition
Fuzzy Point
Fuzzy Set Operations
Fuzzy Sets
hierarchical classifier design
IEEE Trans
Informational Energy
membership
Membership Degrees
objective
partition
pattern recognition methods
pseudometric space analysis
Scatter Matrix
Separation Vector
Set Iteration Counter
Shell Prototypes
substructure
validity
Validity Functionals
Product details
- ISBN 9780849305894
- Weight: 1390g
- Dimensions: 152 x 229mm
- Publication Date: 24 Mar 2000
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Fuzzy set theory - and its underlying fuzzy logic - represents one of the most significant scientific and cultural paradigms to emerge in the last half-century. Its theoretical and technological promise is vast, and we are only beginning to experience its potential. Clustering is the first and most basic application of fuzzy set theory, but forms the basis of many, more sophisticated, intelligent computational models, particularly in pattern recognition, data mining, adaptive and hierarchical clustering, and classifier design.
Fuzzy Sets and their Application to Clustering and Training offers a comprehensive introduction to fuzzy set theory, focusing on the concepts and results needed for training and clustering applications. It provides a unified mathematical framework for fuzzy classification and clustering, a methodology for developing training and classification methods, and a general method for obtaining a variety of fuzzy clustering algorithms.
The authors - top experts from around the world - combine their talents to lay a solid foundation for applications of this powerful tool, from the basic concepts and mathematics through the study of various algorithms, to validity functionals and hierarchical clustering. The result is Fuzzy Sets and their Application to Clustering and Training - an outstanding initiation into the world of fuzzy learning classifiers and fuzzy clustering.
Beatrice Lazzerini, Lakhmi C. Jain, D. Dumitrescu
