Comparative analysis of FCM and HCM algorithm on Iris data set
Pawan Kumar, Deepika Sirohi · International Journal of Computer Applications · 2010
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.