A Study of Fuzzy Clustering Ensemble Algorithm Focusing on Medical Data Analysis

摘要

Unitary clustering algorithm, not well adapted for fuzzy medical data sets, may result in low clustering accuracy and other problems. This paper investigates and compares the effects of various clustering methods to achieve improvements. First, unitary clustering algorithms such as k-means, FANNY, FCM, and etc. are achieved, then FCM algorithm was improved into CFCM algorithm, which increases the accuracy to a certain extent. Second, on this basis, in order to better adapt to the diversity of characteristics of fuzzy medical data, weighted co-association matrix is adopted to achieve integration, and consistency function is designed to present a fuzzy clustering ensemble algorithm. Finally, experiments shows that the Fuzzy Clustering Ensemble Algorithm can solve the problem of low accuracy in unitary clustering algorithm with higher stability, accuracy and robustness.