Abstract
Discriminative pattern mining is known under the names of subgroup discovery, contrast set mining, emerging pattern mining, etc. and has been intensively studied for the last 15 years. Based on the sophisticated techniques developed so far (e.g. branch-and-bound search, minimum support raising, and redundancy elimination including the use of closed patterns), this paper proposes an efficient exact algorithm for finding top-k discriminative patterns that are not redundant and would be of value at a later step in prediction or knowledge discovery. The proposed algorithm is unique in that it conducts depth-first search over enumeration trees in a mirrored form of conventional ones, and by this design we can keep compact the list of candidate top-k patterns during the search and consequently high the minimum support threshold. Experimental results with the datasets from UCI Machine Learning Repository clearly show the efficiency of the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bay, S.D., Pazzani, M.J.: Detecting group differences: mining contrast sets. Data Mining and Knowledge Discovery 5, 213–246 (2001)
Bayardo, R., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery 4, 217–240 (2000)
Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proc. of KDD 1999, pp. 43–52 (1999)
Fürnkranz, J., Gamberger, D., Lavrač, N.: Foundations of Rule Learning. Springer (2012)
Garriga, G.C., Kralj, P., Lavrač, N.: Closed sets for labeled data. J. of Machine Learning Research 9, 559–580 (2008)
Grosskreutz, H., Paurat, D.: Fast and memory-efficient discovery of the top-k relevant subgroups in a reduced candidate space. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part I. LNCS, vol. 6911, pp. 533–548. Springer, Heidelberg (2011)
Grosskreutz, H., Rüping, S., Wrobel, S.: Tight optimistic estimates for fast subgroup discovery. In: Proc. of ECLM/PKDD 2008, pp. 440–456 (2008)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of SIGMOD 2000, pp. 1–12 (2000)
Han, J., Wang, J., Lu, Y., Tzvetkov, P.: Mining top-K frequent closed patterns without minimum support. In: Proc. of ICDM 2002, pp. 211–218 (2002)
Kameya, Y., Sato, T.: RP-growth: top-k mining of relevant patterns with minimum support raising. In: Proc. of SDM 2012, pp. 816–827 (2012)
Kralj Novak, P., Lavrač, N., Webb, G.I.: Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J. of Machine Learning Research 10, 377–403 (2009)
Lavrač, N., Gamberger, D., Jovanoski, V.: A study of relevance for learning in deductive databases. J. of Logic Programming 40, 215–249 (1999)
Morishita, S., Sese, J.: Traversing itemset lattices with statistical metric pruning. In: Proc. of PODS 2000, pp. 226–236 (2000)
Nijssen, S., Guns, T., De Raedt, L.: Correlated itemset mining in ROC space: a constraint programming approach. In: Proc. of KDD 2009, pp. 647–656 (2009)
Pasquier, N., Bastide, Y., Taouil, R., 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)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc. of ICDE 2001, pp. 215–224 (2001)
Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248. AAAI Press (1991)
Soulet, A., Crémilleux, B., Rioult, F.: Condensed representation of emerging patterns. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 127–132. Springer, Heidelberg (2004)
Uno, T., Asai, T., Uchida, Y., Arimura, H.: An efficient algorithm for enumerating closed patterns in transaction databases. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 16–31. Springer, Heidelberg (2004)
Webb, G.I.: Discovering significant patterns. Machine Learning 68, 1–33 (2007)
Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)
Yuan, C., Lim, H., Lu, T.C.: Most relevant explanation in Bayesian networks. J. of Artificial Intelligence Research 42, 309–352 (2011)
Zimmermann, A., De Raedt, L.: Cluster grouping: from subgroup discovery to clustering. Machine Learning 77, 125–159 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Kameya, Y., Asaoka, H. (2013). Depth-First Traversal over a Mirrored Space for Non-redundant Discriminative Itemsets. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-40131-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40130-5
Online ISBN: 978-3-642-40131-2
eBook Packages: Computer ScienceComputer Science (R0)