PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
Tytuł artykułu

Data mining and complex telecommunications problems modeling

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
Strony
115--120
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
Bibliografia
  • [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.
  • [2] M. J. Berry and G. S. Linoff, Mastering Data Mining. The Art and Science of Customer Relationship Management. Wiley, 2000.
  • [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.
  • [4] J. J. Lee and R. Ben-Natan, Integrating Service Level Agreements. Optimizing Your OSS for SLA Delivery. Indianapolis, Indiana: Wiley, 2002.
  • [5] R. Mattison, Data Warehousing and Data Mining for Telecommunications. Boston, London: Artech House, 1997.
  • [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
  • [7] E. Orłowska, “Dynamic information systems”, Ann. Soc. Math. Polon., Ser. IV: Fundam. Informat., vol. 5, no. 1, pp. 101–118, 1982.
  • [8] Z. Pawlak, “Rough sets”, Int. J. Inform. Comput. Sci., vol. 11, pp. 341–356, 1982.
  • [9] Z. Pawlak, Systemy informacyjne. Podstawy teoretyczne. Warszawa: WNT, 1983.
  • [10] Z. Pawlak, “Rough set theory and its applications”, J. Telecommun. Inform. Technol., no. 3, pp. 7–10, 2002.
  • [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.
  • [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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS2-0021-0045
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.