Passer Journal of Basic and Applied Sciences

A new approach to the fuzzy c-means clustering algorithm by automatic weights and local clustering

Document Type : Original Article

Author

University of Garmian, Kalar, Iraq.

Abstract


Clustering is one of the essential strategies in data analysis. In classical solutions, all features are assumed to contribute equally to the data clustering. Of course, some features are more important than others in real data sets. As a result, essential features will have a more significant impact on identifying optimal clusters than other features. In this article, a fuzzy clustering algorithm with local automatic weighting is presented. The proposed algorithm has many advantages such as: 1) the weights perform features locally, meaning that each cluster's weight is different from the rest. 2) calculating the distance between the samples using a non-euclidian similarity criterion to reduce the noise effect. 3) the weight of the features is obtained comparatively during the learning process. In this study, mathematical analyzes were done to obtain the clustering centers well-being and the features' weights. Experiments were done on the data set range to represent the progressive algorithm's efficiency compared to other proposed algorithms with global and local features.

Keywords

  1. References

    1. T. Soni Madhulatha Associate Professor, " AN OVERVIEW ON CLUSTERING METHODS ", IOSR Journal of Engineering Apr. 2012, Vol. 2(4) pp: 719-725 ISSN: 2250-3021 www.iosrjen.org 719 | P a g e, (2012)
    2.  Wang, Fu Zhong, et al. "Research on Transformer Fault Diagnosis Method Based on Artificial Immune Network and Fuzzy C-Means Clustering Algorithm." Applied Mechanics and Materials, vol. 574, Trans Tech Publications, Ltd., pp. 468–473. Crossref. https://doi.org/10.4028/www.scientific.net/AMM.574.468. (2014)
    3. Jain A, Dubes R Algorithms for clustering data. Prentice-Hall, Inc, Upper Saddle River (1988)
    4. Xu, D., Tian, Y. A Comprehensive Survey of Clustering Algorithms. Ann. Data. Sci. 2, 165–193. DOI: 10.1007/s40745-015-0040-1  (2015)
    5. Yin D, Hong L, Davison BD "2111, Structural link analysis and prediction in microblogs the 21st ACM, International Conference on Information and Knowledge Management (CIKM'11),(Glasgow, UK), DOI:                             https://doi.org/10.1145/3269206.3271740, (2018)
    6. Kalantari, H., Ghazanfari, M., Fathian, M., & Shahanaghi, K. A new multi-objective optimization model in the one-layer weighted network through key nodes identification in overlapping communities. Computers & Industrial Engineering, 106413. doi:10.1016/j.cie.2020.106413  , (2020). 
    7. Reddy, Keshava & K., Mrudula. Hard And Fuzzy Clustering Methods: A Comparative Study. ( 2017).
    8.  XB Zhi, JL Fan, and F. Zhao, "Robust local feature weighting hard c-means clustering algorithm," Neurocomputing, vol. 134, pp. 20-29, https://doi.org/10.1007/978-3-642-31919-8_75 (2014)
    9.  KL. Wu and M.S. Yang, "Alternative c-means clustering algorithms," Pattern Recognition, vol. 35, pp. 2267-2278, https://doi.org/10.1016/S0031-3203(01)00197-2. (2002)
    10.   J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Springer US, (1981)
    11. Tabourier, L., Bernardes, D. F., Libert, A. S., Lambiotte, R., Rankmerging: A supervised learning-torank framework to predict links in the extensive social network, The Journal of Systems and Software, 2115, Vol. 88, No. 3, pp. 1-29. https://doi.org/10.1007/s10994-019-05792-4. (2019)
Passer Journal of Basic and Applied Sciences
Volume 3, Issue 1
March 2021
Pages 95-101