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 computational intelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. 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. It aims to organize a collection of data items into clusters, such that items within 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 In this paper, we use FCM (Fuzzy C -mean) clustering algorithm and MFPCM (Modified Fuzzy Possibilistic C mean) clustering algorithm. In this paper we compare the performance analysis of Fuzzy C mean (FCM) clustering algorithm and compare it with Modified Fuzzy possibilistic C mean algorithm. In this we compared FCM and MFPCM algorithm on different data sets. We measure complexity of FCM and MFPCM at different data sets. FCM clustering is a clustering technique which is separated from Modified Fuzzy Possibililstic C mean that employs Possibililstic partitioning.