计算机技术与发展2016，Vol.26Issue(6)：65-68,4.DOI:10.3969/j.issn.1673-629X.2016.06.014
模糊半监督加权聚类算法的有效性评价研究
Study of Clustering Validity Evaluation on Semi-supervised Clustering Algorithm with Feature Discrimination
摘要
Abstract
As the optimal clustering number has great importance in improving the performance of clustering algorithm and expanding the algorithm’s application area,in order to solve the problem of the determination of the optimal clustering number for clustering algorithms effectively and settle the problem that the traditional clustering algorithm often requires prespecified number of clustering,a novel semi-supervised fuzzy clustering algorithm with feature discrimination ( SFFD) is proposed. Firstly,it is used to obtain the clustering result of the measured data,and then four kinds of fuzzy clustering validity evaluation algorithm are adopted for clustering analysis under different clustering number. Finally,by the comparative analysis of various validity evaluation algorithm with experimental data the optimal cluste-ring number was obtained. The experiment based on self-test datasets shows that various clustering validity evaluation algorithm has both the advantages and disadvantages,making a good choice for the clustering validity evaluation algorithm can effectively handle the problem of the determination of the optimal clustering number and enhance the recognition rate effectively for the measured data.关键词
聚类有效性/半监督聚类/算法评估/成对约束/最佳聚类数Key words
clustering validity/semi-supervised clustering/algorithm evaluation/pairwise constraints/optimal clustering number分类
信息技术与安全科学引用本文复制引用
李龙龙,何东健,王美丽..模糊半监督加权聚类算法的有效性评价研究[J].计算机技术与发展,2016,26(6):65-68,4.基金项目
国家“863”高技术发展计划项目(2013AA10230402) （2013AA10230402）
国家自然科学基金资助项目(61402374) （61402374）
陕西工院科研项目(ZK11-34) （ZK11-34）
