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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References
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Elkan, C.: Using the triangle inequality to accelerate k-means. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 147–153 (2003)
Ding, Y., Zhao, Y., Shen, X., Musuvathi, M., Mytkowicz, T.: Yinyang k-means: A drop-in replacement of the classic k-means with consistent speedup. In: International Conference on Machine Learning, pp. 579–587 (2015)
Döring, C., Lesot, M.J., Kruse, R.: Data analysis with fuzzy clustering methods. Comput. Stat. Data Anal. 51(1), 192–214 (2006)
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Ng, R.T., Han, J.: Clarans: a method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 5, 1003–1016 (2002)
Jiulun, F.: New Fuzzy Clustering Algorithm and Clustering Validity. Xidian University, pp. 53–54 (1998)
Peizhuang, W.: Pattern recognition with fuzzy objective function algorithms (james c. bezdek). SIAM Rev. 25(3), 442 (1983)
Zhang, L., Zhong, W., Zhong, C., Wei, L., Liu, X., Pedrycz, W.: Fuzzy c-means clustering based on dual expression between cluster prototypes and reconstructed data. Int. J. Approx. Reason. 90 (2017)
Hathaway, R.J., Hu, Y.: Density-weighted fuzzy c-means clustering. IEEE Trans. Fuzzy Syst. 17(1), 243–252 (2008)
Dong, H., Dong, Y., Zhou, C., Yin, G., Hou, W.: A fuzzy clustering algorithm based on evolutionary programming. Expert Syst. Appl. 36(9), 11792–11800 (2009)
Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., Nandi, A.K.: Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans. Fuzzy Syst. 26(5), 3027–3041 (2018)
Wang, Y., Chen, L., Mei, J.P.: Stochastic gradient descent based fuzzy clustering for large data. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 2511–2518. IEEE (2014)
Havens, T.C., Bezdek, J.C., Leckie, C., Hall, L.O., Palaniswami, M.: Fuzzy c-means algorithms for very large data. IEEE Trans. Fuzzy Syst. 20(6), 1130–1146 (2012)
Parker, J.K., Hall, L.O.: Accelerating fuzzy-c means using an estimated subsample size. IEEE Trans. Fuzzy Syst. 22(5), 1229–1244 (2013)
Mei, J.P., Wang, Y., Chen, L., Miao, C.: Large scale document categorization with fuzzy clustering. IEEE Trans. Fuzzy Syst. 25(5), 1239–1251 (2016)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)
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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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