Abstract
With the rapid proliferation of data across every stream makes raw data practically unusable. In this scenario, clustering has a major impact in grouping similar data into a dataset. This enhances the usability and meaningfulness of data, and further, the quantitative analysis can also be performed. In our existing research, a novel Probabilistic Possibilistic Fuzzy C-Means (PPFCM) clustering method is proposed. In this paper, the proposed PPFCM clustering technique is quantitatively evaluated based on several metrics and the accuracy of the clustering outcome as well as the execution output are investigated. A comparative study is made with the proposed PPFCM clustering with the traditional clustering methods, and the results are plotted. In this work, six benchmark datasets based on different application is used for evaluating the performance of PPFCM clustering method. To measure the productivity of the proposed clustering technique the Sum of Square Error (SSE) metric is used and it is found that the methodology mentioned above performs well for segmentation.
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References
Van Ryzin, J., (ed.): Classification and Clustering: Proceedings of an Advanced Seminar Conducted by the Mathematics Research Center, the University of Wisconsin at Madison, 3–5 May 1976, no. 37. Elsevier (2014)
Liu, A., et al.: Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(1), 102–114 (2017)
Vijaya, J., Sivasankar, E.: Improved churn prediction based on supervised and unsupervised hybrid data mining system. In: Mishra, D., Nayak, M., Joshi, A. (eds.) Information and Communication Technology for Sustainable Development. LNNS, vol. 9, pp. 485–499. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3932-4_51
Bose, I., Chen, X.: Hybrid models using unsupervised clustering for prediction of customer churn. J. Organ. Comput. Electron. Commer. 19(2), 133–151 (2009)
Sivasankar, E., Vijaya, J.: Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network. Neural Comput. Appl. 31(11), 7181–7200 (2018). https://doi.org/10.1007/s00521-018-3548-4
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
Sivasankar, E., Vijaya, J.: Customer segmentation by various clustering approaches and building an effective hybrid learning system on churn prediction dataset. In: Behera, H.S., Mohapatra, D.P. (eds.) Computational Intelligence in Data Mining. AISC, vol. 556, pp. 181–191. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-3874-7_18
Aggarwal, C.C., Reddy, C.K. (eds.): Data Clustering: Algorithms and Applications. CRC Press, Boca Raton (2013)
Huang, Y., Kechadi, T.: An effective hybrid learning system for telecommunication churns prediction. Expert Syst. Appl. 40(14), 5635–5647 (2013)
Rajamohamed, R., Manokaran, J.: Improved credit card churn prediction based on rough clustering and supervised learning techniques. Cluster Comput. 1–13 (2017). https://doi.org/10.1007/s10586-017-0933-1
Selvi, C., Sivasankar, E.: A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach. Soft Comput. 1–16 (2017)
Tech, M.: Fraud detection in credit card by clustering approach
Yadav, A.K., Tomar, D., Agarwal, S.: Clustering of lung cancer data using foggy k-means. In: 2013 International Conference on Recent Trends in Information Technology (ICR-TIT). IEEE (2013)
Badjatiya, P., Kurisinkel, L.J., Gupta, M., Varma, V.: Attention-based neural text segmentation. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 180–193. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76941-7_14
Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2017)
Perey, C.: Social Networking Segmentation: Celebrating Community Diversity in a Framework A W3C Workshop on the Future of Social Networking Position Paper (2008)
McClendon, L., Meghanathan, N.: Using machine learning algorithms to analyze crime data. Mach. Learn. Appl. Int. J. (MLAIJ) 2(1), 1–12 (2015)
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Pal, N.R., et al.: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005)
Grover, N.: A study of various fuzzy clustering algorithms. Int. J. Eng. Res. (IJER) 3(3), 177–181 (2014)
Du, H., Li, Y.: An improved BIRCH clustering algorithm and application in thermal power. In: 2010 International Conference on Web Information Systems and Mining. IEEE (2010)
Moya-Anegn, F., Herrero-Solana, V., Jimnez-Contreras, E.: A con nectionist and multivariate approach to science maps: the SOM, clustering and MDS applied to library and information science research. J. Inf. Sci. 32(1), 63–77 (2006)
Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)
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Vijaya, J., Syed, H. (2021). A Performance Study of Probabilistic Possibilistic Fuzzy C-Means Clustering Algorithm. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_39
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