Temporal Data Mining with Temporal Constraints
Nowadays, methods for discovering temporal knowledge try to extract more complete and representative patterns. The use of qualitative temporal constraints can be helpful in that aim, but its use should also involve methods for reasoning with them (instead of using them just as a high level representation) when a pattern consists of a constraint network instead of an isolated constraint.
In this paper, we put forward a method for mining temporal patterns that makes use of a formal model for representing and reasoning with qualitative temporal constraints. Three steps should be accomplished in the method: 1) the selection of a model that allows a trade off between efficiency and representation; 2) a preprocessing step for adapting the input to the model; 3) a data mining algorithm able to deal with the properties provided by the model for generating a representative output.
In order to implement this method we propose the use of the Fuzzy Temporal Constraint Network (FTCN) formalism and of a temporal abstraction method for preprocessing. Finally, the ideas of the classic methods for data mining inspire an algorithm that can generate FTCNs as output.
Along this paper, we focus our attention on the data mining algorithm.
KeywordsData Mining Temporal Pattern Temporal Relation Constraint Propagation Temporal Constraint
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- 1.Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C, 26–28, pp. 207–216. ACM Press, New York (1993)CrossRefGoogle Scholar
- 4.Campos, M., Cárceles, A., Palma, J., Marín, R.: A general purpose fuzzy temporal information management engine. In: Advances in information and communication technology, in EurAsia-ICT 2002, pp. 93–97 (2002)Google Scholar
- 5.Guil, F., Bosch, A., Bailón, A., Marín, R.: A fuzzy approach for mining generalized frequent temporal patterns. In: Workshop on Alternative Techniques for Data Mining and Knowledge Discovery. Fourth IEEE International Conference on Data Mining (ICDM 2004), Brighton, UK (2004)Google Scholar
- 6.Ho, T.B., Nguyen, T.D., Kawasaki, S., Le, S.Q.: Combining Temporal Abstraction and Data Mining Methods in Medical Data Mining. ch. 7, vol. 3, pp. 198–222. Kluwer Academic Press, Dordrecht (2005)Google Scholar
- 11.Moskovitch, R., Shahar, Y.: Temporal data mining based on temporal abstractions. In: ICDM 2005 Workshop on Temporal Data Mining: Algorithms, Theory and Application. TDM 2005, pp. 113–115 (2005)Google Scholar
- 12.Peek, N., Abu-Hanna, A., Peelen, L.: Acquiring and using temporal knowledge in medicine: an application in chronic pulmonary disease. In: ECAI 2002 Workshop on Knowledge Discovery from (Spatio-)Temporal Data, pp. 44–50 (2002)Google Scholar
- 14.Sudkamp, T.: Discovery of fuzzy temporal associations in multiple data streams. In: Hoffmann, F., Köppen, M., Klawonn, F., Roy, R. (eds.) Advances in Soft Computing, pp. 1–13. Springer, Heidelberg (2005)Google Scholar
- 16.Vincenti, G., Hammell, R.J., Trajkovski, G.: Data mining for imprecise temporal associations. In: 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2005 and First ACIS International Workshop on Self-Assembling Wireless Networks. SNPD/SAWN 2005, pp. 76–81 (2005)Google Scholar
- 17.Winarko, E., Roddick, J.F.: Discovering richer temporal association rules from interval-based data. In: 7th International Conference on Data Warehousing and Knowledge Discovery, DaWak, pp. 315–325 (2005)Google Scholar