Issue No. 03 - March (1985 vol. 7)
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.
T. Gu and B. Dubuisson, "A Loose-Pattern Process Approach to Clustering Fuzzy Data Sets," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 7, no. , pp. 366-372, 1985.