Abstract
User profiling is the process of constructing a normal profile by accumulating the past calling behavior of a user. The technique of clustering focusses on outcome of a structure or an intrinsic grouping in unlabeled data collection. In this paper, our main intention is on building appropriate user profile by applying generalized possibilistic fuzzy c-means (GPFCM) clustering technique. All the call features required to build a user profile is collected from the call detail record of the individual users. The behavioral profile modeling of users is prepared by implementing the clustering on two relevant calling features from the reality-mining dataset. The labels are not present in the dataset and thus we have applied clustering which is an unsupervised approach. Before applying the clustering algorithm, a proper cluster validity analysis has to be done for finding the best cluster value and then the cluster analysis is done using some performance parameters.
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References
Blondel, V.D., Decuyper, A., Krings, G.: A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4(1), 10 (2015)
Subudhi, S., Panigrahi, S.: Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networks. IJSN 11(1/2), 3–11 (2016)
Cox, K.C., Eick, S.G., Wills, G.J., Brachman, R.J.: Brief application description; visual data mining: recognizing telephone calling fraud. Data Min. Knowl. Disc. 1(2), 225–231 (1997)
Alves, R., Ferreira, P., Belo, O., Lopes, J., Ribeiro, J., Cortesão, L., Martins, F.: Discovering telecom fraud situations through mining anomalous behavior patterns. In: Proceedings of the DMBA Workshop, on the 12th ACM SIGKDD (2006)
Hilas, C.S., Mastorocostas, P.A.: An application of supervised and unsupervised learning approaches to telecommunications fraud detection. Knowl.-Based Syst. 21(7), 721–726 (2008)
Hilas, C.S., Kazarlis, S.A., Rekanos, I.T., Mastorocostas, P.A.: A genetic programming approach to telecommunications fraud detection and classification. In: Proceedings of the 2014 International Conference on Circuits, Systems Signal Processing Communication Computer, pp. 77–83 (2014)
Subudhi, S., Panigrahi, S.: Use of possibilistic fuzzy C-means clustering for telecom fraud detection. In: Computational Intelligence in Data Mining, pp. 633–641. Springer, Singapore (2017)
Askari, S., Montazerin, N., Zarandi, M.F.: Generalized possibilistic fuzzy C-means with novel cluster validity indices for clustering noisy data. Appl. Soft Comput. 53, 262–283 (2017)
Eagle, N., Pentland, A.S.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10(4), 255–268 (2006)
Wang, W., Zhang, Y.: On fuzzy cluster validity indices. Fuzzy Sets Syst. 158(19), 2095–2117 (2007)
Deborah, L.J., Baskaran, R., Kannan, A.: A survey on internal validity measure for cluster validation. Int. J. Comput. Sci. Eng. Survey 1(2), 85–102 (2010)
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Ashwini, K., Panigrahi, S. (2020). Application of Generalized Possibilistic Fuzzy C-Means Clustering for User Profiling in Mobile Networks. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_23
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DOI: https://doi.org/10.1007/978-981-15-1084-7_23
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