Comparative Analysis of Parallel K Means and Parallel Fuzzy C Means Cluster Algorithms

In this paper, we give a short review of recent developments in clustering. Clustering is the process of grouping of data, where the grouping is established by finding similarities between data based on their characteristics. Such groups are termed as Clusters. Clustering is a procedure to organizing the objects into groups or clustered together, based on the principle of maximizing the intra-class similarity and minimizing the inter class similarity. A comparative study of clustering algorithms across two different data tests is performed here. The performance of the Parallel k means and parallel fuzzy c means clustering algorithms is compared based upon two metrics. One is an evaluation based on the execution time and the other is on classification error percentage and efficiency. After the experiment on two different data sets, it is concluded that both the algorithms performed well but the computational time of parallel K means is comparatively better than the parallel FCM algorithm. In both sequential and parallel computing FCM performs well but in parallel processing the execution time is considerably decrease compared with Parallel K means.