Guided fuzzy clustering with multi-prototypes

A new fuzzy clustering algorithm using multi-prototype representation of clusters is proposed in this paper to discover clusters with arbitrary shapes and sizes. Intra-cluster non-consistency and inter-cluster overlap are proposed as two mistake measurements to guide the splitting and merging step of the algorithm. In the splitting step, clusters with the largest intra-cluster non-consistency are iteratively split such that the resulting subclusters only contain data from the same class. In the following merging step, subclusters with the largest inter-cluster overlap are iteratively merged until a pre-determined cluster number is achieved. A multi-prototy-pe representation of clusters is used in the merging step to handle the clusters with different size and shapes. Experimental results on synthetic and real datasets demonstrate the effectiveness and robustness of the proposed algorithm.