A Comparative Analysis Between K-Medoids and Fuzzy C-Means Clustering Algorithms for Statistically Distributed Data Points
Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. In the field of data mining, various clustering algorithms are proved for their clustering quality. This paper work deals with, two of the most representative clustering algorithms namely centroid based K-Medoids and representative object based Fuzzy C-Means are described and analyzed based on their basic approach using the distance between two data points.