Affiliations: [a] Department of Computer Science, Vimala College, Thrissur, Kerala, India | [b] Department of Mathematics, St. Thomas College, Pala, Kerala, India
Correspondence:
[*]
Correspoiding author: Ani Davis K, Department of Computer Science, Vimala College, Thrissur, Kerala, India. E-mail: [email protected].
Abstract: Clustering is an unsupervised procedure that divides a set of objects into homogeneous groups. Two types of clustering are possible, Hard clustering and Soft clustering/Fuzzy clustering. Hard clustering is not feasible for complex datasets that contain uncertainty and overlapping clusters, whereas fuzzy clustering efficiently handles it. FCM is sensitive to the initial values and challenging to cluster nonlinear data. A new approach is implemented here with the Fuzzy c-Means (FCM) clustering algorithm to improve the performance. The Kernel function ensures the linear separability of complex clusters by projecting the feature space into a higher dimension and not subject to the initial values. The Kernel-based FCM (KFCM) optimized the clustering. The relevant features are considered for clustering, and it improves the validity of clusters. The irrelevant features blur the clusters and reduce the quality. Silhouette index (SI) and Davies-Bouldin index (DBI) have been used as the evaluation function. The experiments are conducted on two benchmark datasets and one artificial dataset. The result justifies Kernel-based FCM, and the superiority of features reduced Kernel-based FCM clustering over other traditional fuzzy clustering techniques.
