IEA/AIE 2016: Trends in Applied Knowledge-Based Systems and Data Science pp 80-94 | Cite as
Explicit Contrast Patterns Versus Minimal Jumping Emerging Patterns for Lazy Classification in High Dimensional Data
Abstract
Minimal jumping emerging patterns have been proved very useful for classification purposes. Nevertheless, the determination of minimal jumping emerging patterns may require evaluation of candidate patterns, the number of which might be exponential with respect to the dimensionality of a data set. This property may disallow classification by means of minimal jumping emerging patterns in the case of high dimensional data. In this paper, we derive an upper bound on the lengths of minimal jumping emerging patterns and an upper bound on their number. We also propose an alternative approach to lazy classification which uses explicit contrast patterns instead of minimal jumping emerging patterns, but produces the same classification quality as a lazy classifier based on minimal jumping emerging patterns. We argue that our approach, unlike the approach based on minimal jumping emerging patterns, can be applied in the case of high dimensional data.
References
- 1.Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Palamidessi, C., Moniz Pereira, L., Lloyd, J.W., Dahl, V., Furbach, U., Kerber, M., Lau, K.-K., Sagiv, Y., Stuckey, P.J. (eds.) CL 2000. LNCS (LNAI), vol. 1861, pp. 972–986. Springer, Heidelberg (2000)CrossRefGoogle Scholar
- 2.Bailey, J., Ramamohanarao, K.: Mining emerging patterns using tree structures or tree based searches. In: Contrast Data Mining: Concepts, Algorithms, and Applications. Taylor & Francis Group LLC, Boca Raton (2013)Google Scholar
- 3.Dong, G., Li, J., Zhang, X.: Discovering jumping emerging patterns and experiments on real datasets. In: IDC 1999 (1999)Google Scholar
- 4.Fan, H., Kotagiri, R.: An efficient single-scan algorithm for mining essential jumping emerging patterns for classification. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 456–462. Springer, Heidelberg (2002)CrossRefGoogle Scholar
- 5.Kryszkiewicz, M.: Concise representation of frequent patterns based on disjunction-free generators. In: Proceedings of ICDM 2001, pp. 305–312. IEEE Computer Society (2001)Google Scholar
- 6.Kryszkiewicz, M.: Concise representations of frequent patterns and association rules. Prace Naukowe Politechniki Warszawskiej. Elektronika 142 (2002)Google Scholar
- 7.Kryszkiewicz M.: Using generators for discovering certain and generalized decision. In: HIS 2005, pp. 181–186 (2005)Google Scholar
- 8.Li, J., Dong, G., Ramamohanarao, K.: Making use of the most expressive jumping emerging patterns for classification. In: PAKDD 2000, pp. 220–232 (2000)Google Scholar
- 9.Jinyan, L., Guozhu, D., Kotagiri, R., Limsoon, W.: DeEPs: a new instance-based lazy discovery and classification system. ML 54(2), 99–124 (2004)MATHGoogle Scholar
- 10.Skowron A.: Boolean reasoning for decision rules generation. In: ISMIS 1993, pp. 295–305 (1993)Google Scholar
- 11.Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Fast computation of concept lattices using data mining techniques. Proc. KRDB 2000, 129–139 (2000)MATHGoogle Scholar