A clustering algorithm based on maximum entropy principle

Abstract

Aiming at the shortcomings of clustering performance of many traditional text clustering methods, a clustering algorithm based on maximum entropy principle is proposed. The algorithm uses the cosine similarity measure cited in the traditional text clustering algorithm SP-Kmeans, and then introduces the maximal entropy theory to construct the maximal entropy objective function suitable for text clustering. The maximum entropy principle is introduced into the spherical K-mean text clustering Algorithm. The experimental results show that compared with DA-VMFS and SP-Kmeans algorithms, in addressing the large number of text clustering problem. The performance of CAMEP clustering algorithm is greatly improved, and has a good overall performance.