Active function Cross-Entropy Clustering

作者：

Highlights：

• We build highly applicable clustering method of non-linear data.

• We use a f-adapted Gaussian distribution.

• We use Cross-Entropy clustering method instead of EM approach.

• Our algorithm gives better results than classical methods.

摘要

•We build highly applicable clustering method of non-linear data.•We use a f-adapted Gaussian distribution.•We use Cross-Entropy clustering method instead of EM approach.•Our algorithm gives better results than classical methods.

论文关键词：Clustering,Gaussian mixture models,Expectation maximization,Cross-Entropy Clustering,Active curve axis gaussian mixture model

论文评审过程：Received 22 August 2016, Revised 15 November 2016, Accepted 6 December 2016, Available online 7 December 2016, Version of Record 13 December 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.12.011