A clustering algorithm based on maximum entropy principle

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Zhao, Yang^1,2 ; Liu, Fangai^1,2

College of Information Science and Engineering, Shandong Normal University, Jinan Shandong

250358, China^1

Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan Shandong

250358, China^2

关键词: Cosine similarity measures;    K-means;    k-Means algorithm;    Maximal entropy;    Maximum entropy principle;    Objective functions;    Text Clustering;    Text-clustering algorithm;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012064/pdf
DOI  :  10.1088/1742-6596/887/1/012064

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.