Improved fuzzy possibilistic C-means model based on quadratic distance

Abstract: Aiming at the problem of most fuzzy clustering algorithms being sensitive to sample data sets, this sensitivity makes one algorithm run on various kinds of data sets to generate great different clustering results, therefore, we propose improved fuzzy possibilistic C-means based on quadratic distance. We analyze the feature of interval-valued data and introduce mathematic representation method of interval-valued sample data. On the basis of these, we present three measure methods between interval-valued sample data and prototypes and corresponding computing methods of weight matrix, and then propose optimal objective function. The iterative function of centroid and membership and typicality are acquired by constructing Lagrange equation and then it is proved iterative function is convergence by many times iteration. Finally, we provide steps of algorithm. Experiments on two types of three data sets show that algorithm has good performance not only on point prototype but also on interval-valued prototype.

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