A Novel Selective Scale Space based Fuzzy C-means Model for Spatial Clustering

This paper proposed a novel Scale Space Filter based Fuzzy C-Means algorithm for clustering spatial data. The number of clusters, C, in present case is known in advance. The Scale Space filter is used for better separability of the data which are not linearly separable and in the present paper the same is used to selective parameters for betterment to meet the complexity-accuracy tradeoff. The Xie-Beni validity index is used as Objective Function of the model to check the quality of the clusters produced. The Results are tested on Standard iris data. The analysis and comparative study with existing algorithms has also been drawn.