Classification of Liver Samples by Fuzzy Clustering Algorithms

Partition based clustering algorithms are widely used in data clustering. The most popular methods are fuzzy algorithm, Fuzzy c-Means (FCM), and non-fuzzy algorithm, k-means (KM) methods. K-means and Fuzzy c-Means use centroid distance measure and standard Euclidian distance measure respectively. In this work, a comparative study of these algorithms with liver disorder data set from the UCI repository is presented. Repository results were compared with these results. Based on the clustering output criteria the performance of these two algorithms is analyzed in terms of percentage of correctness and classification performance. The experimental results demonstrate that k-means outperforms the Fuzzy c-Means algorithm. Thus the efficiency of k-means is better than that of Fuzzy c-Means.