Abstract
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.
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© 2015 Springer International Publishing Switzerland
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Yang, MS., Chen, YZ., Nataliani, Y. (2015). Bias-Correction Fuzzy C-Regressions Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_26
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DOI: https://doi.org/10.1007/978-3-319-19324-3_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19323-6
Online ISBN: 978-3-319-19324-3
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