COMPARATIVE INVESTIGATIONS AND PERFORMANCE ANALYSIS OF FCM AND MFPCM ALGORITHMS ON IRIS DATA

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Data mining is a computationalintelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. Clustering is aprimary data description method in data mining which group’s most similar data. The data clustering is an important problem in a wide variety offields. Including data mining, pattern recognition, and bioinformatics. It aims to organize a collection of data items into clusters, such that itemswithin a cluster are more similar to each other than they are items in the other clusters. There are various algorithms used to solve this problem Inthis paper, we use FCM (Fuzzy C -mean) clustering algorithm and MFPCM (Modified Fuzzy Possibilistic C - mean) clustering algorithm. In thispaper we compare the performance analysis of Fuzzy C mean (FCM) clustering algorithm and compare it with Modified Fuzzy possibilistic Cmean algorithm. In this we compared FCM and MFPCM algorithm on different data sets. We measure complexity of FCM and MFPCM atdifferent data sets. FCM clustering is a clustering technique which is separated from Modified Fuzzy Possibililstic C mean that employsPossibililstic partitioning