协同极大熵聚类算法  

COLLABORATIVELY MAXIMUM ENTROPY CLUSTERING ALGORITHM

作　　者：江森林[1] 

机构地区：[1]无锡职业技术学院物联网分院,江苏无锡214121

出　　处：《计算机应用与软件》2014年第5期268-271,278,共5页Computer Applications and Software

基　　金：国家自然科学基金项目(60973094);江苏省"333高层次人才培养工程"科研项目(BRA2010128)

摘　　要：极大熵聚类算法(MEC)是基于信息论的新型聚类算法。以不同子集之间的协同关系为出发点,与信息理论中的极大熵原理相结合,通过构造新的极大熵目标函数来改变传统聚类算法中对整个数据集直接聚类的机制。提出一种基于协同的极大熵聚类算法CMEC,它不仅具有较MEC算法更高的聚类精度和更好的泛化性等特点,较之协同模糊聚类算法还具有更好的物理意义。实验结果表明所提出的CMEC算法具有上述优点,其聚类效果比传统的聚类算法有了很大的提高。Maximum entropy clustering algorithm（ MEC） is a new clustering algorithm based on information theory. In this paper, considering the collaboration relation between different subsets as the starting point and combining the MEC principle in information theory, we change the mechanism of clustering directly on entire data set in traditional clustering algorithm by constructing a novel maximum entropy objective function. We propose a collaboration-based maximum entropy clustering（ CMEC）. It has the characteristics of higher clustering accuracy and better generalisation than MEC algorithm,and has better physical sense compared with collaborative fuzzy clustering algorithm. Experimental results show that the proposed CMEC algorithm has the above advantages,it has significant improvement in clustering effect than the traditional clustering algorithm.

关 键 词：聚类算法 极大熵 协同系数 协同的极大熵 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]