Comparative Study on performance of Fuzzy clustering algorithms on Liver and Thyroid Data

Conventional classification methods are difficult to analyze accurate diagnosis without ambiguities due to fast growth in technology. Since the states are vague in medicine comparative crisp ones the fuzzy methods are supportive. As fuzzy tools provide accurate results in various data sets, in this paper, we concentrate on fuzzy based clustering. In this work, a comparative study of these algorithms with Thyroid data set and 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 objective of this paper is to analyze the performance of two popular clustering algorithms FPCM and PFCM for thyroid data and liver data, and to prove that PFCM gives better performance than FPCM for Thyroid Samples and liver samples in terms of percentage of correctness and Classification performance.