Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

Applied Sciences - Tập 7 Số 8 - Trang 798
José Antonio Iglesias1, Agapito Ledezma1, Araceli Sanchis1, Plamen Angelov2
1Computer Science Department, Carlos III University of Madrid, Leganés, Madrid 28918, Spain
2Computing and Communications Department, Lancaster University, Lancaster LA14WA, UK

Tóm tắt

In the competitive telecommunications market, the information that the mobile telecom operators can obtain by regularly analysing their massive stored call logs, is of great interest. Although the data that can be extracted nowadays from mobile phones have been enriched with much information, the data solely from the call logs can give us vital information about the customers. This information is usually related with the calling behaviour of their customers and it can be used to manage them. However, the analysis of these data is normally very complex because of the vast data stream to analyse. Thus, efficient data mining techniques need to be used for this purpose. In this paper, a novel approach to analyse call detail records (CDR) is proposed, with the main goal to extract and cluster different calling patterns or behaviours, and to detect outliers. The main novelty of this approach is that it works in real-time using an evolving and recursive framework.

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Tài liệu tham khảo

Debra, N. (2006). Organizational Behavior: Foundations, Realities & Challenges, South-Western.

Baker, 1987, Evolving concepts of biological control of plant pathogens, Ann. Rev. Phytopathol., 25, 67, 10.1146/annurev.py.25.090187.000435

Ratti, 2006, Mobile landscapes: Using location data from cell phones for urban analysis, Environ. Plan. B Plan. Des., 33, 727, 10.1068/b32047

Zhao, 2017, Mining user attributes using large-scale APP lists of smartphones, IEEE Syst. J., 11, 315, 10.1109/JSYST.2015.2431323

Rosser, 2011, Smartphone applications for pain management, J. Telemed. Telecare, 17, 308, 10.1258/jtt.2011.101102

Pandey, 2013, Smartphone apps as a source of cancer information: Changing trends in health information-seeking behavior, J. Cancer Educ., 28, 138, 10.1007/s13187-012-0446-9

Rawassizadeh, 2016, Scalable daily human behavioral pattern mining from multivariate temporal data, IEEE Trans. Knowl. Data Eng., 28, 3098, 10.1109/TKDE.2016.2592527

Angelov, P. (2013). Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time, John Wiley & Sons, Ltd.

Lara, 2013, A survey on human activity recognition using wearable sensors, IEEE Commun. Surv. Tutor., 15, 1192, 10.1109/SURV.2012.110112.00192

Gravenhorst, 2015, Mobile phones as medical devices in mental disorder treatment: An overview, Pers. Ubiquitous Comput., 19, 335, 10.1007/s00779-014-0829-5

Janecek, 2015, The cellular network as a sensor: From mobile phone data to real-time road traffic monitoring, IEEE Trans. Intell. Transp. Syst., 16, 2551, 10.1109/TITS.2015.2413215

Khan, 2013, Mobile phone sensing systems: A survey, IEEE Commun. Surv. Tutor., 15, 402, 10.1109/SURV.2012.031412.00077

Paraskevopoulos, P., Dinh, T., Dashdorj, Z., and Palpa, Y.T. (2013, January 1–3). Identification and characterization of human behavior patterns from mobile phone data. Proceedings of the NetMob 2013, Cambridge, MA, USA.

Abdallah, 2015, Adaptive mobile activity recognition system with evolving data streams, Neurocomputing, 150, 304, 10.1016/j.neucom.2014.09.074

Motiwalla, 2007, Mobile learning: A framework and evaluation, Comput. Educ., 49, 581, 10.1016/j.compedu.2005.10.011

Blondel, 2015, A survey of results on mobile phone datasets analysis, EPJ Data Sci., 4, 1, 10.1140/epjds/s13688-015-0046-0

Srinivasan, V., Moghaddm, S., Mukherji, A., Rachuri, K.K., Xu, C., and Tapia, Y.E.M. (2014, January 13–17). MobileMiner: Mining Your Frequent Patterns on Your Phone. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA.

Kostakos, V., Ferrerira, D., Goncalves, J., and Hosio, Y.S. (2016, January 12–16). Modelling Smartphone Usage: A Markov State Transition Model. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.

Botta, 2015, Quantifying crowd size with mobile phone and Twitter data, R. Soc. Open Sci., 2, 150, 10.1098/rsos.150162

Jo, 2012, Circadian pattern and burstiness in mobile phone communication, New J. Phys., 14, 13, 10.1088/1367-2630/14/1/013055

Aledavood, 2015, Daily rhythms in mobile telephone communication, PLoS ONE, 10, 1, 10.1371/journal.pone.0138098

Incel, 2013, A review and taxonomy of activity recognition on mobile phones, BioNanoScience, 3, 145, 10.1007/s12668-013-0088-3

Shoaib, 2015, A survey of online activity recognition using mobile phones, Sensors, 15, 2059, 10.3390/s150102059

Ghotekar, 2016, Analysis and Data Mining of Call Detail Records using Big Data Technology, Int. J. Adv. Res. Comput. Commun. Eng., 5, 280, 10.17148/IJARCCE.2016.51264

Moses, E.O., and Festus, Y.O.A. (2017). Multidimensional Analysis and Mining of Call Detail Records Using Pattern Cube Algorithm. Comput. Eng. Inf. Technol., 6.

Leo, 2016, Call detail records to characterize usages and mobility events of phone users, Comput. Commun., 95, 43, 10.1016/j.comcom.2016.05.003

Saramaki, 2015, From seconds to months: Multi-scale dynamics of mobile telephone calls, Eur. Phys. J. B, 88, 164, 10.1140/epjb/e2015-60106-6

Botta, 2017, Analysis of the communities of an urban mobile phone network, PLoS ONE, 12, 1, 10.1371/journal.pone.0174198

Bitar, N., Imran, A., and Refai, Y.H. (2016, January 3–6). A user centric self-optimizing grid-based approach for antenna steering based on call detail records. Proceedings of the 2016 IEEE Wireless Communications and Networking Conference (WCNC), Doha, Qatar.

Kumar, M., Hanumanthappa, M., and Kumar, Y.T.S. (2017, January 19–21). Crime investigation and criminal network analysis using archive call detail records. Proceedings of the 2016 International Conference on Advanced Computing ICoAC, Chennai, India.

Longtong, Y., and Narapiyakul, Y.L. (2016, January 14–17). Suspect tracking based on call logs analysis and visualization. Proceedings of the 2016 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, Thailand.

Angelov, P. (2009). Evolving fuzzy systems. Encyclopedia of Complexity and Systems Science, Springer.

Angelov, P., Ramezani, R., and Zhou, Y.X. (2008, January 1–8). Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system. Proceedings of the International Joint Conference on Neural Networks, Hong Kong, China.

Guedes, 2016, An evolving approach to unsupervised and Real-Time fault detection in industrial processes, Expert Syst. Appl., 63, 134, 10.1016/j.eswa.2016.06.035

Kangin, D., Angelov, P., Iglesias, J.A., and Sanchis, Y.A. (November, January 29). Evolving Classifier TEDAClass for Big Data. Proceedings of the 2015 INNS Conference on Big Data, San Francisco, CA, USA.

Iglesias, 2010, Human Activity Recognition Based on Evolving Fuzzy Systems, Int. J. Neural Syst., 20, 355, 10.1142/S0129065710002462

Grinstein, G., Plaisant, C., Laskowski, S., O’Connell, T., and Scholtz, J. (2008, January 19–24). VAST 2008 Challenge: Introducing mini-challenges. Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, Columbus, OH, USA.

Mantzaris, V., and Highan, Y.D.J. (2016). Asymmetry through time dependency. Eur. Phys. J. B Condens. Matter Complex Syst., 89.

Heard, 2016, Convergence of Monte Carlo distribution estimates from rival samplers, Stat. Comput., 26, 1147, 10.1007/s11222-015-9595-0

Leung, C.K., Johnston, P., Carmichael, C.L., Xing, R.R., and Hung-Cheung Yuen, Y.D.S. (2017). Interactive Visual Analytics of Big Data. Ontologies and Big Data Considerations for Effective Intelligence, IGI Global.

Angelov, P.P. (2002). Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems, Springer.

Angelov, P.P. (2007). Machine Learning. (Collaborative Systems). (WO2008053161), U.S. Patent.

Sadeghi-Tehran, P., Angelov, P., and Ramezani, Y.R. (July, January 28). A Fast Approach to Autonomous Detection, Identification, and Tracking of Multiple Objects in Video Streams under Uncertainties. Proceedings of the 2010 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), Dortmund, Germany.

Iglesias, 2010, Evolving classification of agents behaviors: A general approach, Evol. Syst., 1, 161, 10.1007/s12530-010-9008-8

Ruan, D., Chen, G., Kerrer, E.E., and Wets, Y.G. (2005). Intelligent Data Mining: Techniques and Applications, Springer Science & Business Media.

Hawkins, D.M. (1980). Identification of Outliers, Chapman and Hall London.

2010, An approach to online identification of Takagi-Sugeno fuzzy models, IEEE Trans. Syst. Man Cybern. Part B, 34, 484

Lughofer, E. (2011). Interpretability Issues in EFS. Evolving Fuzzy Systems—Methodologies, Advanced Concepts and Applications, Springer.