Fuzzy c-shells clustering algorithm. (August 2020)

• Record Type: Journal Article
• Authors: Pratiwi, N B I; Saputro, D R S
• Is Part Of: Journal of physics. Volume 1613(2020)
• Publication Date: 2020-08
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1613/1/012006
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Clustering is a process to classify data into some clusters or classes so then data in same cluster have maximum similarity and data between clusters have minimum similarity. Generally, clustering is separated into two methods, hierarchical and non-hierarchical methods. Non-hierarchical methods are separated as soft clustering and hard clustering. Non-Hierarchical methods that mostly used is K-Means algorithm. K-Means grouping object into certain member and not included in different clusters. Another approaches in clustering technique which is based on fuzzy sets theory is known as fuzzy clustering. Fuzzy clustering is one kind of soft clustering that also widely used because of many advantages than the hard clustering. Several varieties of fuzzy clustering are Fuzzy C-Means (FCM) and Fuzzy C-Shells (FCS). FCM is characterized by centre of cluster and FCS uses radius as additional parameter. This paper concerns FCS clustering algorithm using review of several references. Hence, FCS is characterized by centre of cluster and radius.