Data analysis and flow graphs

• Autorzy: Pawlak Z.
• Języki publikacji: EN

Abstrakty

EN

In this paper we present a new approach to data analysis based on flow distribution study in a flow network. Branches of the flow graph are interpreted as decision rules, whereas the flow graph is supposed to describe a decision algorithm. We propose to model decision processes as flow graphs and analyze decisions in terms of flow spreading in the graph.

Słowa kluczowe

EN

data mining; data independence; flow graph; Bayes' rule

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2004
• Tom: nr 3
• Strony: 18--22
• Opis fizyczny: Bibliogr. 7 poz., il.

Twórcy

autor

Pawlak Z.

• Institute for Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka st 5, 44-100 Gliwice, Poland. University of Information Technology and Management, Newelska st 6, 01-447 Warsaw, Poland,

Bibliografia

• [1] M. Berthold and D. J. Hand, Intelligent Data Analysis|an Introduction. Berlin [etc.]: Springer-Verlag, 1999.
• [2] L. R. Ford and D. R. Fulkerson, Flows in Networks. Princeton [etc.]: Princeton University Press, 1962.
• [3] J. Łukasiewicz, „Die logishen Grundlagen der Wahrscheinilchkeitsrechnung" (Kraków, 1913), in Jan Łukasiewicz-Selected Works, L. Borkowski, Ed. Amsterdam [etc.], North Holland Publ., Warsaw: Polish Scienti_c Publ., 1970.
• [4] M. Kryszkiewicz, H. Rybiński, and M. Muraszkiewicz, „Data mining methods for telecom providers", Institute of Computer Sciences, Warsaw University of Technology, Research Report 28/02 and „MOST-mobile open society thorough wirless telecommunications technology for mobile society", M. Muraszkiewicz, Ed., Warsaw University of Technology, 2003, pp. 210-220.
• [5] Z. Pawlak, „Probability, truth and ow graphs", in RSKD – Rough Sets in Knowledge Discovery, Proc., A. Skowron and M. Szczuka, Eds., Warsaw, Poland, 2003, pp. 1-9.
• [6] Z. Pawlak, „Flow graphs and decision algorithms", in Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, G. Wang, Y. Yao, and A. Skowron, Eds., Lecture Notes in Artifficial Intelligence. Berlin: Springer, 2003, vol. 2639, pp. 1-10.
• [7] Bayes's Theorem, Proceedings of the British Academy, R. Swinburne, Ed. Oxford University Press, 2002, vol. 113.