Data mining and complex telecommunications problems modeling
DOI:
https://doi.org/10.26636/jtit.2003.3.181Keywords:
decision support systems, telecommunications, dynamic information system, temporal data miningAbstract
The telecommunications operators have to manage one of the most complex systems developed by human beings. Moreover, the new technological developments, the convergence of voice and data networks and the broad range of services still increase this complexity. Such complex object as telecommunication network requires advanced software tools for their planning and management. Telecommunications operators collect large volumes of the data in various databases. They realize that the knowledge in these huge databases might significantly improve various organizational strategic and operational decisions. However, this knowledge is not given explicitly, it is hidden in data. Advanced methods and algorithms are being developed for knowledge extracting. In this paper we will focus on using data mining for solving selected problems in telecommunication industry. We will provide a systematic overview of various telecommunications applications.
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References
[1] J.-L. Amat, "Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management", J. Telecommun. Inform. Technol., no. 3, pp. 11-16, 2002.
View in Google Scholar
[2] M. J. Berry and G. S. Linoff, Mastering Data Mining. The Art and Science of Customer Relationship Management. Wiley, 2000.
View in Google Scholar
[3] S. Greco, B. Matarazzo, and R. Słowiński, "Rough sets theory for multicriteria decision analysis", Eur. J. Oper. Res., vol. 129, pp. 1-47, 2001.
View in Google Scholar
[4] J. J. Lee and R. Ben-Natan, Integrating Service Level Agreements. Optimizing Your OSS for SLA Delivery. Indianapolis, Indiana: Wiley, 2002.
View in Google Scholar
[5] R. Mattison, Data Warehousing and Data Mining for Telecommunications. Boston, London: Artech House, 1997.
View in Google Scholar
[6] K. Morik and M. Scholz, "The MiningMart approach to knowledge discovery in databases", in Handbook of Intelligent IT, Ning Zhong and Jiming Liu, Eds. IOS Press, 2003.
View in Google Scholar
[7] E. Orłowska, "Dynamic information systems", Ann. Soc. Math. Polon., Ser. IV: Fundam. Informat., vol. 5, no. 1, pp. 101-118, 1982.
View in Google Scholar
[8] Z. Pawlak, "Rough sets", Int. J. Inform. Comput. Sci., vol. 11, pp. 341-356, 1982.
View in Google Scholar
[9] Z. Pawlak, Systemy informacyjne. Podstawy teoretyczne. Warszawa: WNT, 1983.
View in Google Scholar
[10] Z. Pawlak, "Rough set theory and its applications", J. Telecommun. Inform. Technol., no. 3, pp. 7-10, 2002.
View in Google Scholar
[11] M. Shawa, C. Subramaniama, G. Tana, and M. Welgeb, "Knowledge management and data mining for marketing", Decis. Supp. Syst., vol. 31, no. 1, pp. 127-137, 2001.
View in Google Scholar
[12] C.-P. Wei and I.-T. Chiu, "Turning telecommunications call detail to churn prediction: a data mining approach", Expert Syst. Appl., vol. 23, pp. 103-112, 2002.
View in Google Scholar
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