Abstract
Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery.In this paper, we present the integration of Association rules and Classification rules using Concept Lattice. This gives more accurate classifiers for Classification. The algorithm used is incremental in nature. Any increase in the number of classes, attributes or transactions does not require the access to the previous database. The incremental behavior is very useful in finding classification rules for real time data such as image processing. The algorithm requires just one database pass through the entire database. Individual classes can have different support threshold and pruning conditions such as criteria for noise and number of conditions in the classifier.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994 (1994)
Carpineto, C., Romano, G.: Galois: An order-theoretic approach to conceptual clustering. In: Proceedings of ICML 1993, Amherst, pp. 33–40 (1993)
Fu, H., Fu, H., Njiwoua, P., Nguifo, E.M.: A Comparative study of FCA- based Supervised Classification of Algorithms. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 313–320. Springer, Heidelberg (2004)
Godin, R., Missaoui, R., Alaoui, H.: Incremental concept formation algorithm based on Galois lattice. Computational Intelligence 11, 246–267 (1995)
Ganter, B., Wille, R.: Formal Concept Analysis, Mathematical Foundations. Springer, Heidelberg (1999)
Hu, K., Lu, Y., Zhou, L., Shi, C.: Integrating Classification and Association Rule Mining: A Concept Lattice framework. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 443–447. Springer, Heidelberg (1999)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques
Liquiere, M., Mephu Nguifo, E.: LEGAL: Learning with Galois lattice
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proceedings of KDD 1998 (1998)
Mehta, M., Agrawal, R., Rissanen, J.: SLIQ: A Fast Scalable Classifier for Data Mining. In: Proc. of the fifth Int’l. Conference on Extending Database Technolog
Merz, C.J., Murthy, P.: UCI repository of machine learning database (1996), ftp://ftp.ics.uci.edu/pub/machine-learning-databases/
Zaki, M.J.: Generating Non-Reduntant Association Rules. In: Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining (KDD 2000) (2000)
Pasquier, N., Bastide, Y., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)
Sahami, M.: Learning Classification Rules using Lattices. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912. Springer, Heidelberg (1995)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: SIGMOD 1996 (1996)
Quinlan, J.R.: Induction of decision tree. Machine Learning (1986)
Quinlan, J.R.: C4.5: Program for machine learning. Morgan Kaufmann, San Francisco (1992)
Xie, Z., Hsu, W., Liu, Z., Lee, M.L.: Concept Lattice based Composite Classifiers for High Predictability. Journal of Experimental and Theoretical Artificial Intelligence 14, 143–156 (2002)
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Gupta, A., Kumar, N., Bhatnagar, V. (2005). Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_2
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DOI: https://doi.org/10.1007/11510888_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
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