Comparative Analysis of Fuzzy C- Mean and Modified Fuzzy Possibilistic C -Mean Algorithms in Data Mining

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. 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 Possibililstic 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 possibililstic 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. The FCM employs fuzzy portioning such that a point can belong to all groups with different membership grades between 0 and 1.