Comparative analysis of FCM and HCM algorithm on Iris data set

Abstract

Clustering is a primary data description method in data mining which group's most similar data. The data clustering is an important problem in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. There are various algorithms used to solve this problem. This paper presents the comparison of the performance analysis of Fuzzy C mean (FCM) clustering algorithm and compares it with Hard C Mean (HCM) algorithm on Iris flower data set. We measure Time complexity and space Complexity of FCM and HCM at Iris data [1] set. FCM clustering [2, 3] is a clustering technique which is separated from Hard C Mean that employs hard partitioning. The FCM employs fuzzy portioning such that a point can belong to all groups with different membership grades between 0 and 1.