A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model

In this paper we propose a new clustering algorithm which combines the FCM clustering algorithm with the supervised learning normal mixture model; we call the algorithm as the FCM-SLNMM clustering algorithm. The FCM-SLNMM clustering algorithm consists of two steps. The FCM algorithm was applied in the first step. In the second step the supervised learning normal mixture model was applied and the clustering result of the first step was used as training data. The experiments on the real world data from the UCI repository show that the supervised learning normal mixture model can improve the performance of the FCM algorithm sharply, and which also show that the FCM-SLNMM perform much better than the unsupervised learning normal mixture model and other comparison clustering algorithms. This indicates that the FCM-SLNMM algorithm is an effective clustering algorithm.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Wei Wang, Chunheng Wang, Xia Cui, Ai Wang
• Keywords: Clustering Algorithm, Computer Vision, ICPR 2008, Learning Normal Mixture, Normal Mixture Model