The use of quantitative association rules in cellular network planning

• Autorzy: Okoniewski M.
• Języki publikacji: EN

Abstrakty

EN

This paper describes the problem of planning cellular network base stations with optimization to traffic requirements. This research problem was a main incentive to add some development to the theory of association rules. The new form of quantitative and multi-dimensional association rules, unlike other approaches, does not require the discretization of real value attributes as a preprocessing step. They are discovered with data driven algorithm that gives precise and complete results and has polynomial complexity for a given dimensionality.

Słowa kluczowe

EN

data mining; quantitative association rules; knowledge discovery process; cellular network planning

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2003
• Tom: nr 3
• Strony: 121--124
• Opis fizyczny: Bibliogr. 9 poz., rys.

Twórcy

autor

Okoniewski M.

• Institute of Computer Science, Warsaw University of Technology,

Bibliografia

• [1] P. Gawrysiak and M. Okoniewski, Applying Data Mining Methods for Cellular Network Planning, Intelligent Information Systems. Springer-Physica Verlag, 2000.
• [2] P. Gawrysiak, M. Okoniewski, and H. Rybiński, Regression – Yet Another Clustering Method, Intelligent Information Systems. Springer-Physica Verlag, 2001.
• [3] V. Geade and O. Günther, “Multidimensioanal access methods”, ACM Comput. Surv., vol. 30, no. 2, 1997.
• [4] Y. Lindell and Y. Aumann, “Theory of quantitative association rulet with statistical validation” in Proc. Conf. SIGKDD, San Diego, USA, 1999.
• [5] R. J. Miller and Y. Yang, “Association rules over interval data” In Proc. Conf. ACM SIGMOD’97, 1997.
• [6] M. Okoniewski, “Discovery of multi-dimensional quantitative association rules”. Ph.D. thesis. Warsaw University of Technology, Warsaw, 2001.
• [7] M. Okoniewski, Ł. Gancarz, and P. Gawrysiak, “Mining multi-dimensional quantitative associations” in Rule-based Data Mining Workshop, Conf. INAP 2001, Tokio, Japan, 2001.
• [8] R. Srikant and R. Agrawal, “Mining quantitative association rules In large relational tables” in Proc. Conf. SIGMOD, Montreal, Canada, 1996.
• [9] A. Skowron and S. H. Nguyen, “Quantization of real value attributes: rough set and boolean reasoning approach”. Tech. Rep., Warsaw University of Technology, Warsaw, 1995.