Inference process in decision support systems with incomplete knowledge

Agnieszka Nowak-Brzezińska, Tomasz Jach

Abstract


Authors propose new approach to vagueness problem in decision support systems. To achieve optimal solutions by clustering decision rules, cluster analysis methods are being used. This paper states the results of experiments regarding the influence of Agnes’ algorithm to the quality of clustering process.

Keywords


uncertain knowledge; cluster analysis; clustering; decision support system

Full Text:

PDF (Polski)

References


Nowak-Brzezińska A., Wakulicz-Deja A.: Analiza efektywności wnioskowania w złożonych bazach wiedzy. Systemy Wspomagania Decyzji, 2007.

Bazan J., Nguyen H. S., Nguyen S. H., Synak P., Wróblewski J.: Rough set algorithms in classification problems. [in:] Polkowski L., Lin T. Y., Tsumoto S.(eds.): Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Heidelberg: Physica-Verlag, 2000, s. 49÷88.

Pawlak Z.: Rough set approach to knowledge-based decision suport. European Journal of Operational Research. 16 May 1997, s. 48÷57.

Latkowski R.: Wnioskowanie w oparciu o niekompletny opis obiektów (praca magisterska). Warszawa Wydział Matematyki, Informatyki i Mechaniki Uniwersytetu Warszawskiego, 2001.

Zadeh L.A., Kacprzyk J.: Fuzzy logic for the management of uncertainty. New York: John Wiley & Sons, 1992.

Geiger D., Heckerman D.: Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence. 1996, s. 45÷74.

Towell G. G., Shavlika J.W.: Knowledge-based artificial neural networks. Artificial Intelligence. 1994, s. 119÷165.

Bazan J. G., Szczuka M. S., Wróblewski J. A new version of rough set exploration system. [in:] Alpigini J. J. et al.(eds.): Third International Conference on Rough Sets and Current Trends in Computing RSCTC. Malvern, PA: Springer-Verlag, 2002, s. 397÷414.

Frank A., Asuncion A.: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2010.

Kaufman L., Rousseeuw P. J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York 1990.

Jain A. K., Dubes R. C.: Algorithms for clustering data. New Jersey: Prentice Hall, 1988.

Salton G.: Automatic Information Organization and Retreival. New York, USA: McGraw-Hill, 1975.

Osiński S., Weiss D.: Carrot2: An Open Source Framework for Search Results Clustering, 2004.

Kumar V., Tan P. N., Steinbach M.: Introduction to Data Mining. Addison-Wesley, 2006.

Myatt G. J.: Making Sense of Data A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey : John Wiley and Sons, Inc., 2007.

Wakulicz-Deja A.: Podstawy systemów wyszukiwania informacji. Analiza metod. Warszawa: Akademicka Oficyna Wydawnicza PLJ, 1995.




DOI: http://dx.doi.org/10.21936/si2011_v32.n2A.273