Abstract:

Aiming at the problem of most fuzzy clustering algorithms being sensitive to point data sets, and shortcoming of unobvious clustering results and so on, we propose improved fuzzy possibilistic C-means (FPCM) 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 iterative function is proved convergence. Finally, steps of algorithm are provided. 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.