Abstract
Multi-relational concept discovery aims to find the relational rules that best describe the target concept. An important challenge that relational knowledge discovery systems face is intractably large search space and there is a trade-off between pruning the search space for fast discovery and generating high quality rules. Combining ILP approach with conventional association rule mining techniques provides effective pruning mechanisms. Due to the nature of Apriori algorithm, the facts that do not have common attributes with the target concept are discarded. This leads to efficient pruning of search space. However, under certain conditions, it fails to generate transitive rules, which is an important drawback when transitive rules are the only way to describe the target concept. In this work, we analyze the effect of incorporating unrelated facts for generating transitive rules in an hybrid relational concept discovery system, namely C2D, which combines ILP and Apriori.
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References
Džeroski, S.: Multi-relational data mining: an introduction. SIGKDD Explorations 5(1), 1–16 (2003)
Muggleton, S.: Inductive Logic Programming. In: The MIT Encyclopedia of the Cognitive Sciences (MITECS). MIT Press, Cambridge (1999)
Kavurucu, Y., Senkul, P., Toroslu, I.H.: Confidence-based concept discovery in multi-relational data mining. In: International Conference on Data Mining and Applications (ICDMA), Hong Kong, pp. 446–451 (March 2008)
Kavurucu, Y., Senkul, P., Toroslu, I.H.: Aggregation in confidence-based concept discovery for multi-relational data mining. In: IADIS European Conference on Data Mining (ECDM), Amsterdam, Netherland, pp. 43–50 (July 2008)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press (1996)
Quinlan, J.R.: Learning logical definitions from relations. Mach. Learn. 5(3), 239–266 (1990)
Muggleton, S.: Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4), 245–286 (1995)
Srinivasan, A.: The aleph manual (1999)
Dehaspe, L., Raedt, L.D.: Mining association rules in multiple relations. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 125–132. Springer, Heidelberg (1997)
Toprak, S.D., Senkul, P., Kavurucu, Y., Toroslu, I.H.: A new ILP-based concept discovery method for business intelligence. In: ICDE Workshop on Data Mining and Business Intelligence (April 2007)
Michalski, R., Larson, J.: Inductive inference of vl decision rules. In: Workshop on Pattern-Directed Inference Systems, Hawaii, SIGART Newsletter, vol. 63, pp. 33–44. ACM Press, New York (1997)
Hinton, G.: UCI machine learning repository kinship data set (1990), http://archive.ics.uci.edu/ml/datasets/Kinship
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Kavurucu, Y., Senkul, P., Toroslu, I.H. (2009). Analyzing Transitive Rules on a Hybrid Concept Discovery System. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_27
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DOI: https://doi.org/10.1007/978-3-642-02319-4_27
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