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Abstract
The pattern mining community has shifted its attention from local pattern mining to pattern set mining. The task of pattern set mining is concerned with finding a set of patterns that satisfies a set of constraints and often also scores best w.r.t. an optimisation criteria. Furthermore, while in local pattern mining the constraints are imposed at the level of individual patterns, in pattern set mining they are also concerned with the overall set of patterns. A wide variety of different pattern set mining techniques is available in literature. The key contribution of this paper is that it studies, compares and evaluates such search strategies for pattern set mining. The investigation employs concept-learning as a benchmark for pattern set mining and employs a constraint programming framework in which key components of pattern set mining are formulated and implemented. The study leads to novel insights into the strong and weak points of different pattern set mining strategies.
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References
Bringmann, B., Nijssen, S., Tatti, N., Vreeken, J., Zimmermann, A.: Mining sets of patterns. In: Tutorial at ECMLPKDD 2010 (2010)
Bringmann, B., Zimmermann, A.: Tree\(^{\mbox{2}}\) - decision trees for tree structured data. In: Jorge, A., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 46–58. Springer, Heidelberg (2005)
Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3, 261–283 (1989)
De Raedt, L., Guns, T., Nijssen, S.: Constraint programming for itemset mining. In: KDD, pp. 204–212. ACM, New York (2008)
De Raedt, L., Zimmermann, A.: Constraint-based pattern set mining. In: SDM. SIAM, Philadelphia (2007)
Frank, A., Asuncion, A.: UCI machine learning repository (2010),http://archive.ics.uci.edu/ml
Guns, T., Nijssen, S., De Raedt, L.: k-Pattern set mining under constraints. CW Reports CW596, Department of Computer Science, K.U.Leuven (October 2010),https://lirias.kuleuven.be/handle/123456789/278655
Kearns, M.J., Vazirani, U.V.: An introduction to computational learning theory. MIT Press, Cambridge (1994)
Khiari, M., Boizumault, P., Crémilleux, B.: Constraint programming for mining n-ary patterns. In: Cohen, D. (ed.) CP 2010. LNCS, vol. 6308, pp. 552–567. Springer, Heidelberg (2010)
Knobbe, A., Crémilleux, B., Fürnkranz, J., Scholz, M.: From local patterns to global models: The lego approach to data mining. In: Fürnkranz, J., Knobbe, A. (eds.) Proceedings of LeGo 2008, an ECMLPKDD 2008 Workshop (2008)
Knobbe, A.J., Ho, E.K.Y.: Pattern teams. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 577–584. Springer, Heidelberg (2006)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD, pp. 80–86 (1998)
Nijssen, S., Guns, T., De Raedt, L.: Correlated itemset mining in ROC space: a constraint programming approach. In: KDD, pp. 647–656. ACM, New York (2009)
Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)
Rückert, U., De Raedt, L.: An experimental evaluation of simplicity in rule learning. Artif. Intell. 172(1), 19–28 (2008)
Siebes, A., Vreeken, J., van Leeuwen, M.: Item sets that compress. In: Ghosh, J., Lambert, D., Skillicorn, D.B., Srivastava, J. (eds.) SDM, pp. 395–406. SIAM, Philadelphia (2006)
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Authors and Affiliations
Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001, Leuven, Belgium
Tias Guns, Siegfried Nijssen & Luc De Raedt
- Tias Guns
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- Siegfried Nijssen
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- Luc De Raedt
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Editors and Affiliations
Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, 518055, Shenzhen, China
Joshua Zhexue Huang
Faculty of Engineering and Information Technology, Center for Quantum Computation and Intelligent Systems, Data Sciences and Knowledge Discovery Lab, University of Technology Sydney, 2007, Sydney, NSW, Australia
Longbing Cao
Department of Computer Science and Engineering, University of Minnesota, 55455, Minneapolis, MN, USA
Jaideep Srivastava
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Guns, T., Nijssen, S., De Raedt, L. (2011). Evaluating Pattern Set Mining Strategies in a Constraint Programming Framework. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20847-8_32
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