title: A loose-pattern process approach to clustering fuzzy data sets.
abstract: 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.
authors.affiliation: University of Technology of Compiegne, 60206 Compiegne Cedex, France.
authors.forename: T
authors.initials: T
authors.lastname: Gu
authors.forename: B
authors.initials: B
authors.lastname: Dubuisson