现代电子技术2007，Vol.30Issue(2)：152-153,156,3.
Kernel-based Maximum Entropy Clustering
Kernel-based Maximum Entropy Clustering
JIANG Wei 1QU Jiao 1LI Benxi2
作者信息
- 1. School of Mathematics,Liaoning Normal University,Dalian,116029,China
- 2. Department of Base Science,Liaoning Technical University,Fuxin,123000,China
- 折叠
摘要
Abstract
With the development of Support Vector Machine (SVM),the "kernel method" has been studied in a general way.In this paper,we present a novel Kernel-based Maximum Entropy Clustering algorithm (KMEC).By using mercer kernel functions,the proposed algorithm is firstly map the data from their original space to high dimensional space where the data are expected to be more separable,then perform MEC clustering in the feature space.The experimental results show that the proposed method has better performance in the non-hyperspherical and complex data structure.关键词
kernel clustering/maximum entropy/KMECKey words
kernel clustering/maximum entropy/KMEC分类
信息技术与安全科学引用本文复制引用
JIANG Wei,QU Jiao,LI Benxi..Kernel-based Maximum Entropy Clustering[J].现代电子技术,2007,30(2):152-153,156,3.
