An Initialization Method for Fuzzy C-means Algorithm Using Subtractive Clustering
Qing Yang, Dongxu Zhang, Feng Tian · 2010
In clustering methods, the estimation of the optimal number of clusters is significant for subsequent analysis. As a simple clustering method, the fuzzy c-means algorithm (FCM) has been widely discussed and applied in pattern recognition and machine learning. However, the FCM could not guarantee unique clustering result because initial cluster number is chosen randomly. As the number of clusters is randomly chosen, the iterative amount is large and the result of the classification is unstable. An initialization method for FCM algorithm using subtractive clustering is presented in this paper. The experiments show that the modified algorithm can improve the speed, and reduce the iterative amount. At the same time, this method can make the results of the classification more stable and have higher precision.