Clustering Mixed Data Points Using Fuzzy C- Means Clustering Algorithm for Performance Analysis

Clustering plays an outstanding role in data mining research. Among the various algorithms for clustering, most of the researchers used the Fuzzy C-Means algorithm (FCM) in the areas like computational geometry, data compression and vector quantization, pattern recognition and pattern classification. In this research, a simple and efficient implementation of FCM clustering algorithm is presented. Three types of inputs are given to algorithm. The data points are first distributed manually, the statistical distributions Normal and Uniform are anther two methods by using the Box-Muller formula. The algorithm is analyzed based on their clustering quality. The behavior of the algorithm depends on the number of data points as well as on the number of cluster. The performance of the algorithm is investigated during different execution of the program on the input data points. The execution time for each cluster and total elapsed time to cluster all the data points is also analyzed and the results are compared with one another.