A novel cluster validity index for fuzzy C-means algorithm

To overcome the main problem of the cluster number in many clustering applications, a new clustering approach with improved morphology similarity distance and the novel cluster validity index is proposed in this paper. An optimized morphology similarity distance based on the Standard Euclidean distance and ReliefF algorithm is used to create a new validity index, which can balance the intra-cluster consistency and inter-cluster consistency. The proposed validity index is combined with fuzzy C-means to produce a creative algorithm simply named the OMS-OSC algorithm. Experimental results obtained using different artificial data sets and real-world data sets show that the new algorithm can not only yield good performance but also detect the correct cluster number.