Abstract
Authors propose a new approach in the optimization of inference processes in decision support systems with incomplete knowledge. The idea is based on clustering large set of rules from knowledge bases as long as it is necessary to find a relevant rule as quickly as possible. This work is highly focused on the results of experiments regarding the influence of Agnes’ algorithm parameters on the quality of the clustering process. Additionally, the authors present the results of the experiments regarding the optimal amount of groups formed by decision rules.
Keywords
- cluster analysis
- clustering
- decision support systems
- incomplete knowledge
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References
Abonyi, J., Feil, B.: Cluster Analysis for Data Mining and System Identification. Birkhäuser Verlag AG, Basel (2007)
Bazan, J.G., Szczuka, M.S., Wróblewski, J.: A new version of rough set exploration system. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 397–404. Springer, Heidelberg (2002)
Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice Hall, New Jersey (1988)
Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
Myatt, G.J.: Making Sense of Data A Practical Guide to Exploratory Data Analysis and Data Mining. John Wiley and Sons, Inc., New Jersey (2007)
Nowak-Brzezińska, A., Wakulicz-Deja, A., Simiński, R.: Knowledge representation for composited knowledge base. Intelligent Information Systems (2008)
Salton, G.: Automatic Information Organization and Retreival. McGraw-Hill, New York (1975)
Cios, K.J., Pedrycz, W., Świniarski, R.W., Kurgan, L.A.: Data mining. A Knowledge Discovery Approach. Springer Science+Business Media, Heidelberg
Grzymała-Busse, J.: A New Version of the Rule Induction System LERS. Fundamenta Informaticae 31(1), 27–39 (1997)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://archive.ics.uci.edu/ml
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Wakulicz-Deja, A., Nowak-Brzezińska, A., Jach, T. (2011). Inference Processes in Decision Support Systems with Incomplete Knowledge. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_78
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DOI: https://doi.org/10.1007/978-3-642-24425-4_78
Publisher Name: Springer, Berlin, Heidelberg
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