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A Loose-Pattern Process Approach to Clustering Fuzzy Data Sets
March 1985 (vol. 7 no. 3)
pp. 366-372
Tao Gu, University of Technology of Compiegne, 60206 Compiegne Cedex, France.
B. Dubuisson, University of Technology of Compiegne, 60206 Compiegne Cedex, France.
A loose-pattern process approach to clustering sets consists of three main computations: loose-pattern reject option, tight-pattern classifcation, and loose-pattern assigning classes. The loose-pattern rejection is implemented using a rule based on q nearest neighbors of each point. Two clustering methods, GLC and OUPIC, are introduced as tight-pattern clustering techniques. The decisions of loose-pattern assigning classes are related to a heuristic membership function. The function and experiments with one set is discussed.
Citation:
Tao Gu, B. Dubuisson, "A Loose-Pattern Process Approach to Clustering Fuzzy Data Sets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 3, pp. 366-372, March 1985, doi:10.1109/TPAMI.1985.4767669
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