计算机应用研究2012，Vol.29Issue(11)：4093-4096,4.DOI:10.3969/j.issn.1001-3695.2012.11.023
MFASSC:基于间隔Fisher分析的半监督聚类方法
MFASSC: semi-supervised clustering approach based marginal Fisher analysis
摘要
Abstract
For the problem of clustering with high dimensional data, this paper presented a novel semi-supervised clustering approach based marginal fisher analysis ( MFASSC). All the data were first projected to a low-dimensional space using marginal Fisher analysis (MFA) and then clustered by PCSKM in the projected space. The algorithm effectively utilized supervised information to integrate dimensionality reduction and semi-supervised clustering. According to the clustering results above, it conducted dimensionality reduction operations and clustering analysis alternately until convergence. Experimental results show MFASSC can effectively deal with the high-dimensional data and simultaneously improve the clustering performance.关键词
半监督聚类/成对约束/间隔Fisher分析/数据降维Key words
semi-supervised clustering/ pairwise constraint / marginal Fisher analysis/ dimensionality reduction分类
信息技术与安全科学引用本文复制引用
李森,刘希玉..MFASSC:基于间隔Fisher分析的半监督聚类方法[J].计算机应用研究,2012,29(11):4093-4096,4.基金项目
国家自然科学基金资助项目(61170038) （61170038）
山东省自然科学基金资助项目(ZR2011FM001) （ZR2011FM001）
山东省软科学重大项目(2010RKMA2005) （2010RKMA2005）
