A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm

Jyoti Arora; Meena Tushir; Shivank Kumar Dadhwal

doi:10.4108/eetsis.v10i3.2057

Authors

Jyoti Arora Maharaja Surajmal Institute of Technology, New Delhi, India
Meena Tushir Maharaja Surajmal Institute of Technology, New Delhi, India
Shivank Kumar Dadhwal Maharaja Surajmal Institute of Technology, New Delhi, India

DOI:

https://doi.org/10.4108/eetsis.v10i3.2057

Keywords:

fuzzy c-means, possibilistic c-means, possibilistic fuzzy c-means, suppression possibilistic fuzzy c-means

Abstract

Possibilistic fuzzy c-means (PFCM) is one of the most widely used clustering algorithm that solves the noise sensitivity problem of Fuzzy c-means (FCM) and coincident clusters problem of possibilistic c-means (PCM). Though PFCM is a highly reliable clustering algorithm but the efficiency of the algorithm can be further improved by introducing the concept of suppression. Suppression-based algorithms employ the winner and non-winner based suppression technique on the datasets, helping in performing better classification of real-world datasets into clusters. In this paper, we propose a suppression-based possibilistic fuzzy c-means clustering algorithm (SPFCM) for the process of clustering. The paper explores the performance of the proposed methodology based on number of misclassifications for various real datasets and synthetic datasets and it is found to perform better than other clustering techniques in the sequel, i.e., normal as well as suppression-based algorithms. The SPFCM is found to perform more efficiently and converges faster as compared to other clustering techniques.

References

Zhang, Q., Yang, L.T., Yan, Z., Chen, Z., and Li., P. (2018) An efficient deep learning model to predict cloud workload for industry informatics. IEEE Transactions on Industrial Informatics 14(7): 3170-3178.

Bezdek, J.C., Keller, J., Krishnapuram, R., and Pal, N. R. (1999) Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer.

Sarki, R., Ahmed, K., Wang, H., Zhang, Y., and Wang, K. (2022) Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Transactions on Scalable Information Systems 9(4):1-11.

Fang, C., and Liu, H. (2021) Research and Application of Improved Clustering Algorithm in Retail Customer Classification. Symmetry 13(10).

Mittal, H., Pandey, A.C., Saraswat, M., Kumar, S., Pal, R., and Modwel. G. (2021) A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets. Multimedia Tools and Applications.

Zheng, H., He, J., Huang, G., Zhang, Y., and Wang, H. (2019) Dynamic optimisation based fuzzy association rule mining method. International Journal of Machine Learning and Cybernetics 10:2187–2198.

Singh, R., Zhang, Y., Wang, H., Miao, Y., and Ahmed, K. (2020) Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study. EAI Endorsed Transactions on Scalable Information Systems 8(3): 1-18.

Wang, H., Chen, Y., and Dong, S. (2017). Research on efficient‐efficient routing protocol for WSNs based on improved artificial bee colony algorithm. IET Wireless Sensor Systems, 7(1), 15-20.

Dong, S., Zhou, D., Ding, W., and Gong, J. (2014) Flow Cluster Algorithm Based on Improved K-means Method. IETE Journal of Research 59(4): 326-333.

Bora, D.J., and Gupta., A.K. (2014) Clustering approach towards image segmentation: An Analytical Study. International Journal of Research in Computer Applications and Robotics 2(7): 115-124.

Dunn, J.C. (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated cluster. Journal of Cybernetics 32–57.

Bezdek, J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York.

Krishnapuram, R., and Keller, J.M. (1993) A possibilistic approach to clustering IEEE Transactions on Fuzzy Systems, 1(2): 98–110.

Fan, J.L., Zhen, W-Z., and Xie, W-X. (2003) Suppressed fuzzy c-means clustering algorithm. Pattern Recognition Letters 24:1607–1612.

Yu, H., Fan, J., and Lan, R. (2019) Suppressed possibilistic c-means clustering algorithm. Applied Soft Computing, 80: 845-872.

Pal, N.R., Pal, K., and Bezdek, J.C. (1997) A mixed c-means clustering model. in: Proceedings of the IEEE International Conference on Fuzzy Systems, Spain 11–21.

Pal, N.R., Pal, K., Keller, J., and Bezdek, J.C. (2005) A possibilistic Fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 13(4): 517–530.

Ozdemir, O. and Kaya, A. (2018) Effect of parameter selection on fuzzy clustering. Journal of Applied Sciences of Mehmet Akif Ersoy University 2(1): 22-33.

Ozdemir, O., and Kaya, A. (2018) K-medoids and fuzzy c-means algorithms for clustering CO2 emissions of Turkey and other OECD countries. Applied Ecology and Environmental Research, 16(3): 2513-2526.

Blake, C., Keough, E., and Merz, C.J. UCI Repository of Machine Learning Database. [online] http://www.ics.uci.edu/mlearn/Mlrepository.html.

Arora, J., and Tushir, M. (2017) A new kernel-based possibilistic intuitionistic fuzzy c-means clustering. International Journal of Artificial Intelligence and Soft Computing 6(4): 306-325.