Abstract
With the increasing amount of data, the calculation of the distance is complicated in fuzzy c-means. In this paper, we propose a new global membership scaling FCM (GMSFCM). The data will be divided into two types at each iteration: the first one is the in-cluster samples, which will not change their clusters in next iteration; the second one is the out-of-cluster samples, which will change their clusters in next iteration; then a new scheme for scaling the membership degrees is suggested to boost the effect of the in-cluster samples and weaken the effect of the out-of-cluster samples. However, the filtering of the in-cluster and the out-of-cluster samples often leads to a high computational complexity per iteration. Thus, we will use triangle inequality to avoid unnecessary distance calculations. The new scheme not only improves the convergence but also keeps the quality for fuzzy clustering.
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Zhou, S., Li, D. (2021). New Global Membership Scaling Fuzzy C-Means Clustering Algorithm. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_22
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DOI: https://doi.org/10.1007/978-3-030-70665-4_22
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