Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm - Kulkarni, AB; Bonde, SV; Kulkarni, UV (International Journal of Computer Sciences and Engineering, 2025)
The paper proposes a new supervised fuzzy clustering algorithm based on inter-class and intra-class clustering technique to create the clusters i.e. fuzzy hyperspheres (FHSs) and pruning technique to prune redundant FHSs which are camouflaged by the other FHSs of the same class. The proposed clustering technique finds the centroid and the width of the FHS based on the spread of inter-class patterns and then groups intra-class patterns using fuzzy membership function, whereas the pruning technique creates the optimal number of FHSs from the FHSs created in the earlier stage. This algorithm is independent of parameters, limits the interference of outliers and converges quickly to create an optimal number of clusters. The main feature of the proposed fuzzy clustering algorithm is that it camouflages the clustered patterns giving 100% accuracy for any training dataset. The performance of the proposed algorithm is tested on eleven benchmark datasets and it is observed that the proposed algorithm results are superior and comparable with classifiers using clustering algorithm.