Abstract
Emerging patterns (EPs) are associations of features whose frequencies increase significantly from one class to another. They have been proven useful to build powerful classifiers and to help establishing diagnosis. Because of the huge search space, mining and representing EPs is a hard task for large datasets. Thanks to the use of recent results on condensed representations of frequent closed patterns, we propose here an exact condensed representation of EPs. We also give a method to provide EPs with the highest growth rates, we call them strong emerging patterns (SEPs). In collaboration with the Philips company, experiments show the interests of SEPs.
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References
Bailey, J., Manoukian, T., Ramamohanarao, K.: Fast algorithms for mining emerging patterns. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 39–50. Springer, Heidelberg (2002)
Birkhoff, G.: Lattices theory. American Mathematical Society 25 (1967)
Boulicaut, J.F., Bykowski, A., Rigotti, C.: Free-sets: a condensed representation of boolean data for the approximation of frequency queries. DMKD journal (2003)
Calders, T., Goethals, B.: Mining all non-derivable frequent itemsets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, p. 74. Springer, Heidelberg (2002)
Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Knowledge Discovery and Data Mining, pp. 43–52 (1999)
Dong, G., Zhang, X., Wong, W., Li, J.: CAEP: Classification by aggregating emerging patterns. In: Discovery Science, pp. 30–42 (1999)
Li, J., Ramamohanarao, K.: The space of jumping emerging patterns and its incremental maintenance algorithms. In: Proc. ICML (2000)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)
Pasquier, N., Bastide, Y., Taouil, T., 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)
Zhang, X., Dong, G., Ramamohanarao, K.: Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In: KDD (2000)
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© 2004 Springer-Verlag Berlin Heidelberg
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Soulet, A., Crémilleux, B., Rioult, F. (2004). Condensed Representation of Emerging Patterns. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_16
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DOI: https://doi.org/10.1007/978-3-540-24775-3_16
Publisher Name: Springer, Berlin, Heidelberg
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