An initialization method for multi-type prototype fuzzy clustering
Gao Xinbo, Xue Zhong, Jie Li, Xie Wei-xin · 2002
Fuzzy clustering is an important branch of unsupervised classification, and has been widely used in pattern recognition and image processing. However, most existing fuzzy clustering algorithms are sensitive to initialization, and strongly depend on the number of clusters, which limits their applications. Moreover, it these algorithms also need to know the type and number of prototypes in advance in multi-type prototype fuzzy clustering. To overcome these limitations, a method for acquiring a priori knowledge about the clustering prototype is proposed in this paper, which obtains better performance in initializing multi-type prototype fuzzy clustering.