Research on Subspace Possibilistic Clustering Mechanism
Qing Guan, Zhaohong Deng, Shitong Wang · Jisuanji gongcheng · 2011
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.