计算机工程Issue(6)：165-171,7.DOI:10.3969/j.issn.1000-3428.2015.06.030
基于局部和全局信息的改进聚类算法
Improved Clustering Algorithm Based on Local and Global Information
摘要
Abstract
Traditional K-means clustering algorithm is sensitive to the initialization. Spectral clustering operates on the similar matrix,and severely affects the cluster result. Clustering with local and global regularization does not take the distribution of data set into consideration. To solve this problem,this paper introduces the dispersion matrix to improve the clustering on the base of local and global regularization. The proposed algorithm takes the distribution of data set into consideration which combines the local information and dispersion matrix. The global optimal information is considered, and then it gets the final optimization problem which can be solved by the eigenvalue decomposition of a spare symmetric matrix. Several mentioned algorithms are tested on UCI machine learning data sets and public data mining data sets. Experimental results and comparison results show the greater performance of the proposed algorithm.关键词
K-means算法/谱聚类/离散度矩阵/特征分解/UCI数据集Key words
K-means algorithm/spectral clustering/dispersion matrix/characteristic decomposition/UCI data set分类
信息技术与安全科学引用本文复制引用
许小龙,王士同,梅向东..基于局部和全局信息的改进聚类算法[J].计算机工程,2015,(6):165-171,7.基金项目
江苏省自然科学基金资助项目(BK2011417)。 （BK2011417）
