Bias-Correction Fuzzy C-Regressions Algorithm

In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. However, the FCM algorithm is usually affected by initializations. Incorporating FCM into switching regressions, called the fuzzy c-regressions (FCR), has also the same drawback as FCM, where bad initializations may cause difficulties in obtaining appropriate clustering and regression results. In this paper, we proposed the bias-correction fuzzy c-regressions (BFCR) algorithm by incorporating bias-correction FCM (BFCM) into switching regressions. Some numerical examples were used to compare the proposed algorithm with some existing fuzzy c-regressions methods. The results indicated the superiority and effectiveness of the proposed BFCR algorithm.