000 03990cam a2200361Ii 4500
001 9780429175428
008 180706s2000 xx o 000 0 eng d
020 _a9780429175428
_q(e-book : PDF)
020 _z9780849305894
_q(hardback)
024 7 _a10.1201/9781482273977
_2doi
035 _a(OCoLC)1021290256
050 4 _aQA248.5
_bL399 2000
072 7 _aCOM051240
_2bisacsh
072 7 _aCOM059000
_2bisacsh
082 0 4 _a511.322
100 1 _aLazzerini, Beatrice,
_eauthor.
_915714
245 1 0 _aFuzzy Sets & their Application to Clustering & Training /
_cBeatrice Lazzerini, Lakhmi C. Jain, D. Dumitrescu.
250 _aFirst edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2000.
300 _a1 online resource
490 0 _aInternational Series on Computational Intelligence
505 0 _apart I Basic aspects of fuzzy set theory -- chapter 1 Fuzzy Sets -- chapter 2 Properties of fuzzy set operations. Disjointness and fuzzy partitions -- chapter 3 Algebraic properties of the families of fuzzy sets -- chapter 4 Metric concepts for fuzzy sets -- chapter 5 Entropy and informational energy of finite fuzzy partitions -- chapter 6 Fuzziness and nonfuzziness measures -- part II Supervised fuzzy learning classifiers -- chapter 7 Fuzzy neural classifiers. Fuzzy perceptron algorithm and some relatives -- chapter 8 Fuzzy learning algorithms using squared criterion function -- part III One-level fuzzy partitional prototype-based clustering -- chapter 9 One-level clustering. Cluster substructure of a fuzzy class -- chapter 10 Other one-level clustering methods -- chapter 11 Linear cluster detection -- chapter 12 Adaptive algorithms for one-level fuzzy clustering -- chapter 13 Advanced adaptive algorithms -- chapter 14 Cluster validity -- chapter 15 Advanced cluster validity functionals -- chapter 16 Convergence of fuzzy clustering algorithms -- part IV Fuzzy discriminant analysis and hierarchical fuzzy clustering -- chapter 17 Fuzzy discriminant analysis and related clustering criteria -- chapter 18 Fuzzy hierarchical clustering -- chapter 19 Fuzzy simultaneous clustering.
520 2 _a"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."--Provided by publisher.
650 0 4 _aIntelligent Systems
_913643
650 0 4 _aComputer Engineering
_910164
650 0 _aNeural computers.
_94963
650 0 _aComputer engineering.
_910164
700 1 _aDumitrescu, D.,
_eauthor.
_915715
700 1 _aJain, Lakhmi C.,
_eauthor.
_915716
776 0 8 _iPrint version:
_z9780849305894
_w(DLC) 99088763
856 4 0 _uhttps://www.taylorfrancis.com/books/9781482273977
_zClick here to view.
942 _cEBK
999 _c71069
_d71069