Maximum-entropy clustering algorithm and its global convergence analysis

摘要

Constructing a batch of differentiable entropy functions to uniformly approximate an objec- tive function by means of the maximum-entropy principle, a new clustering algorithm, called maximum- entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft gener- alization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.