Abstract
The aim of the paper is to cluster partial decision rules using Agglomerative Hierarchical Clustering (AHC) algorithm. Partial decision rules are constructed by greedy algorithm. We study how exact and partial decision rules clustered by AHC algorithm influence on inference process on knowledge bases. Clusters of rules are a way of modularization of knowledge bases in Decision Support Systems. The results of rules clustering are searched during the inference process only the most relevant rules, what makes the inference process faster. Results of experiments present how different factors (rule length, number of facts given as an input knowledge, value of α parameter used to construct partial decision rules, number of rules in a given knowledge base) can influence on the efficiency of inference process.
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References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Ćwik, J., Koronacki, J.: Statistical learning systems. Wydawnictwa Naukowo-Techniczne (2005) (in Polish)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, New York (1990)
Luger, G.: Artificial Intelligence, Structures and Strategies for Complex Problem Solving. Addison-Wesley, Reading (2002)
Moshkov, M.J., Piliszczuk, M., Zielosko, B.: On partial covers, reducts and decision rules. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets VIII. LNCS, vol. 5084, pp. 251–288. Springer, Heidelberg (2008)
Moshkov, M.J., Piliszczuk, M., Zielosko, B.: Partial covers, reducts and decision rules in rough sets: theory and applications. Studies in Computational Intelligence, vol. 145. Springer, Heidelberg (2009)
Nowak, A., Simiński, R., Wakulicz-Deja, A.: Towards modular representation of knowledge base. Advances in Soft Computing 5, 421–428 (2006)
Nowak, A., Simiński, R., Wakulicz-Deja, A.: Knowledge representation for composited knowledge bases. In: Kłopotek, M.A., Przepiórkowski, A., Wierzchoń, S.T., Trojanowski, K. (eds.) Intelligent Information Systems, pp. 405–414 (2008)
Nowak, A., Wakulicz-Deja, A.: The concept of the hierarchical clustering algorithms for rules based systems. Advances in Soft Computing 31, 565–570 (2005)
Nowak, A., Wakulicz-Deja, A.: The analysis of inference efficiency in composited knowledge bases. In: Proceedings of Decision Support Systems Conference, pp. 101–108. Zakopane, Poland (2008) (in Polish)
Nowak, A., Wakulicz-Deja, A.: The inference processes on composited knowledge bases. In: Kłopotek, M.A., Przepiórkowski, A., Wierzchoń, S.T., Trojanowski, K. (eds.) Intelligent Information Systems, pp. 415–422 (2008)
Nowak, A., Wakulicz-Deja, A., Bachliński, S.: Optimization of speech recognition by clustering of phones. Fundamenta Informaticae 72, 283–293 (2006)
Pawlak, Z.: Rough Sets – Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Zielosko, B., Piliszczuk, M.: Greedy algorithm for attribute reduction. Fundamenta Informaticae 85(1-4), 549–561 (2008)
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Nowak, A., Zielosko, B. (2009). Clustering of Partial Decision Rules. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_18
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DOI: https://doi.org/10.1007/978-3-642-00563-3_18
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