Abstract
This paper aims to propose an Evolutionary version of Evidential C-Mean (E2CM) clustering method based on a Variable string length Artificial Bee Colony (VABC) algorithm. In the E2CM, the centers of clusters are encoded in form of a population of strings with variable length to search optimal number of clusters as well as locations of centers based on the VABC, by minimizing objective function non-specificity, in which the assignment of objects to the population of cluster centers are performed by the ECM. One significant merit of the E2CM is that it can automatically create a credal partition without requiring the number of clusters as a priority. A numerical example is used to intuitively verify our conclusions.
This work is supported in part by the National Natural Science Foundation of China (51676034), and by the Key Project of Yunnan Power Grid Co. Ltd. (YNYJ2016043).
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Su, Zg., Zhou, Hy., Wang, Ph., Zhao, G., Zhao, M. (2018). E2CM: An Evolutionary Version of Evidential C-Means Clustering Algorithm. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds) Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science(), vol 11069. Springer, Cham. https://doi.org/10.1007/978-3-319-99383-6_29
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DOI: https://doi.org/10.1007/978-3-319-99383-6_29
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