Maximum-entropy clustering algorithm and its global convergence analysis

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

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