A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

Abstract The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM ensemble approach that takes local information into account (LI_BIFCM) overcome these challenges increase quality. First, various membership matrices are created after running multiple times, based on the randomization initial cluster centers, vertical performed using maximum principle. Second, each execution FCM, sample points K-nearest neighbors, horizontal performed. Multiple ensembles can be clustering. Finally, final results obtained by combining ensembles. Twelve data sets were chosen for testing from both synthetic real sources. LI_BIFCM outperformed four traditional algorithms three in experiments. Furthermore, weak correlation with parameters, indicating suggested technique robust.