NDLI: MECA: maximum entropy clustering algorithm

Content Provider	IEEE Xplore Digital Library
Author	Karayiannis, N.B.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. Eng., Houston Univ., TX, USA (Karayiannis, N.B.)
Abstract	This paper presents a new approach to fuzzy clustering, which provides the basis for the development of the maximum entropy clustering algorithm (MECA). The derivation of the proposed algorithm is based on an objective function incorporating a measure of the entropy of the membership functions and a measure of the distortion between the prototypes and the feature vectors. This formulation allows the gradual transition from a maximum uncertainty or minimum selectivity phase to a minimum uncertainty or maximum selectivity phase during the clustering process. Such a transition is achieved by controlling the relative effect of the maximization of the membership entropy and the minimization of the distortion between the prototypes and the feature vectors. The IRIS data set provides the basis for evaluating the proposed algorithms and comparing their performance with that of competing techniques.<>
Starting Page	630
Ending Page	635
File Size	516491
Page Count	6
File Format	PDF
ISBN	078031896X
DOI	10.1109/FUZZY.1994.343658
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Entropy Clustering algorithms Prototypes Fuzzy sets Iterative algorithms Nearest neighbor searches Partitioning algorithms Distortion measurement Iris Design engineering
Content Type	Text
Resource Type	Article

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