Fuzzy clustering is an important branch of unsupervised classification

and has been widely used in patternrecognition and image processing. However

most of exiting fuzzy clustering algorithms are sensitive to initialization

andstrongly depend on the number of clusters

which limits their applications. Moreover

it also needs to know the type and number of prototypes in advance in multi-type prototype fuzzy clustering. To overcome these limitation

a metthod for acquiring apriori knowledge about clustering prototype is proposed in this paper

which obtains better performance in initializing multitype prototype fuzzy clustering.