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Fuzzy sets and their application to ...

Dumitrescu, D. (1949-)

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Fuzzy sets and their application to clustering and training /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Fuzzy sets and their application to clustering and training // D. Dumitrescu, B. Lazzerini, L.C. Jain
Author:	Dumitrescu, D.
other author:	Lazzerini, Beatrice,
Description:	622 pages ;24 cm
Subject:	Fuzzy sets - Periodicals. -
ISBN:	0849305896

Fuzzy sets and their application to clustering and training /
Dumitrescu, D.1949-

Fuzzy sets and their application to clustering and training /D. Dumitrescu, B. Lazzerini, L.C. Jain - 622 pages ;24 cm

Includes bibliographical references (pages 599-600) and index

Basic aspects of fuzzy set theory --

"Fuzzy Sets and their Application to Clustering & Training offers a comprehensive introduction to fuzzy set theory, focused 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."--Jacket

ISBN: 0849305896Subjects--Topical Terms:

958598
Fuzzy sets
--Periodicals.

LC Class. No.: QA248.5 / .D86 2000

Dewey Class. No.: 511.3/22

Fuzzy sets and their application to clustering and training /
LDR:02902cam 22002654a 4500 001 2241114
003 OCoLC
005 20160712064822.0
008 211112s2000 flu b 001 0 eng
020 $a 0849305896 $q (alk. paper)
020 $a 9780849305894 $q (alk. paper)
035 $a (OCoLC)43050210 $z (OCoLC)60453581
035 $a 43050210
040 $a DLC $b eng $c DLC $d BAKER $d BTCTA $d YDXCP $d OCLCG $d IG# $d EXW $d ILU $d BDX $d OCLCF $d OCLCO $d OCLCQ $d UtOrBLW
042 $a pcc
050 0 0 $a QA248.5 $b .D86 2000
082 0 0 $a 511.3/22 $2 21
100 1 $a Dumitrescu, D. $q (Dumitru), $d 1949- $3 3496636
245 1 0 $a Fuzzy sets and their application to clustering and training / $c D. Dumitrescu, B. Lazzerini, L.C. Jain
264 # 1 $a Boca Raton, FL : $b CRC Press, $c 2000
300 $a 622 pages ; $c 24 cm
336 $a text $b txt $2 rdacontent
337 $a unmediated $b n $2 rdamedia
338 $a volume $b nc $2 rdacarrier
504 $a Includes bibliographical references (pages 599-600) and index
505 0 0 $t Basic aspects of fuzzy set theory -- $t Fuzzy Sets -- $t Fuzzy sets and fuzzy points -- $t Axioms for operations on fuzzy sets -- $t Triangular norms and conorms -- $t Ordinal sums -- $t Min and max operators for intersection and union -- $t Fuzzy complement -- $t Properties of fuzzy set operations. Disjointness and fuzzy partitions -- $t Properties of fuzzy set operations -- $t Disjoint fuzzy sets and binary fuzzy partitions -- $t Disjoint families. Disjoint sequences of fuzzy sets -- $t Fuzzy n-partitions of fuzzy sets -- $t Refinement relation for fuzzy partitions -- $t Algebraic join of fuzzy partitions -- $t Algebraic properties of the families of fuzzy sets -- $t Posets and lattices -- $t Lattice structure of L(X) -- $t MV-algebras -- $t Residuated lattices -- $t Regular basic triples -- $t Matching operator as residuated implication -- $t Metric concepts for fuzzy sets -- $t Basic notions -- $t Diameter of a fuzzy set -- $t Fuzzy ball -- $t Distance with respect to a fuzzy class -- $t Entropy and informational energy of finite fuzzy partitions -- $t Entropy of a complete system of fuzzy events -- $t Entropy and conditional entropy of a fuzzy partition -- $t Properties of fuzzy partition entropy -- $t Informational energy of fuzzy partitions -- $t Conditional informational energy -- $t Informational energy induced by a subadditive measure -- $t Fuzziness and nonfuzziness measures -- $t Measures of fuzziness -- $t Nonfuzziness measures -- $t Correlation between fuzzy sets -- $t Other approaches -- $t Supervised fuzzy learning classifiers -- $t Fuzzy neural classifiers. Fuzzy perceptron algorithm and some relatives
520 1 $a "Fuzzy Sets and their Application to Clustering & Training offers a comprehensive introduction to fuzzy set theory, focused 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."--Jacket
650 # 0 $a Fuzzy sets $v Periodicals. $3 958598
650 # 0 $a Cluster analysis $v Congresses. $3 543178
700 1 # $a Lazzerini, Beatrice, $d 1953- $3 3496637
700 1 # $a Jain, L. C $3 3496638