Efficiently Depth-First Minimal Pattern Mining
- 2.7k Downloads
Condensed representations have been studied extensively for 15 years. In particular, the maximal patterns of the equivalence classes have received much attention with very general proposals. In contrast, the minimal patterns remained in the shadows in particular because of their difficult extraction. In this paper, we present a generic framework for minimal patterns mining by introducing the concept of minimizable set system. This framework addresses various languages such as itemsets or strings, and at the same time, different metrics such as frequency. For instance, the free and the essential patterns are naturally handled by our approach, just as the minimal strings. Then, for any minimizable set system, we introduce a fast minimality check that is easy to incorporate in a depth-first search algorithm for mining the minimal patterns. We demonstrate that it is polynomial-delay and polynomial-space. Experiments on traditional benchmarks complete our study.
KeywordsPattern mining condensed representation minimal pattern
Unable to display preview. Download preview PDF.
- 3.Zaki, M.J.: Generating non-redundant association rules. In: KDD, pp. 34–43 (2000)Google Scholar
- 4.Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD, pp. 80–86 (1998)Google Scholar
- 8.Lo, D., Khoo, S.C., Li, J.: Mining and ranking generators of sequential patterns. In: SDM, pp. 553–564. SIAM (2008)Google Scholar
- 9.Li, J., Li, H., Wong, L., Pei, J., Dong, G.: Minimum description length principle: Generators are preferable to closed patterns. In: AAAI, pp. 409–414 (2006)Google Scholar
- 10.Arimura, H., Uno, T.: Polynomial-delay and polynomial-space algorithms for mining closed sequences, graphs, and pictures in accessible set systems. In: SDM, pp. 1087–1098. SIAM (2009)Google Scholar
- 11.Calders, T., Goethals, B.: Depth-first non-derivable itemset mining. In: SDM, pp. 250–261 (2005)Google Scholar
- 13.Murakami, K., Uno, T.: Efficient algorithms for dualizing large-scale hypergraphs. In: ALENEX, pp. 1–13 (2013)Google Scholar
- 14.Hamrouni, T.: Key roles of closed sets and minimal generators in concise representations of frequent patterns. Intell. Data Anal. 16(4), 581–631 (2012)Google Scholar
- 18.Gao, C., Wang, J., He, Y., Zhou, L.: Efficient mining of frequent sequence generators. In: WWW, pp. 1051–1052. ACM (2008)Google Scholar
- 20.Zeng, Z., Wang, J., Zhang, J., Zhou, L.: FOGGER: an algorithm for graph generator discovery. In: EDBT, pp. 517–528 (2009)Google Scholar