计算机工程2011，Vol.37Issue(5)：224-226,3.
子空间可能性聚类机制研究
Research on Subspace Possibilistic Clustering Mechanism
摘要
Abstract
The obvious shortcomings of Possibilistic C-Means(PCM) algorithm is that the performance will be significantly reduced for high dimensional data sets and it can not effectively identify the useful subspace embedded in the high dimensional space. In order to overcome the weakness, the subspace clustering mechanism is introduced and the Subspace Possibilistic Clustering(SPC) algorithm is presented. It not only has the advantages of PCM algorithm but also has the characteristic of the classic subspace clustering algorithms. Namely, it has good adaptability to high dimensional data, and can detect the subspaces for each cluster effectively. Simulation experiments with synthetic and real data sets demonstrate the effectiveness and the merits of SPC.关键词
高维数据/子空间聚类/特征加权/可能性聚类Key words
high dimensional data/ subspace clustering/ feature weighting/ possibilistic clustering分类
信息技术与安全科学引用本文复制引用
关庆,邓赵红,王士同..子空间可能性聚类机制研究[J].计算机工程,2011,37(5):224-226,3.基金项目
国家自然科学基金资助项目(60903100) （60903100）
江苏省自然科学基金资助项目(BK2009067) （BK2009067）
