TRUMiner: Temporal Rules Mining in Multivariate and Heterogeneous Series Databases
Abstract
This work presents TRUMiner (Temporal RUles Miner), a new algorithm to mine temporal rules from multivariate time series. Our approach deals with missing data and heterogeneous time series, including variables with different numbers of observations. Furthermore, TRUMiner returns detailed temporal rules which allow referring to the respective occurrences in the original time series. We evaluated the TRUMiner algorithm on international trade multivariate data from several sources. Early results show the applicability of our algorithm to heterogeneous time series datasets, simplifying data integration and data pre-processing.
Keywords:
Data Mining, Temporal Rules, Time Series Mining, Multivariate Time Series
References
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de Oliveira, F. A., Costa, R. L., Goldschmidt, R. R., and Cavalcanti, M. C. (2017). Mineração de regras de associação multirrelação em grafos: Direcionando o processo de busca. In SBBD (Short Papers), pages 270-275.
Han, J., Kamber, M., and Pei, J. (2011). Data mining: Concepts and techniques. (3rd ed), Morgan Kauffman.
Harms, S. K. and Deogun, J. S. (2004). Sequential association rule mining with time lags. Journal of Intelligent Information Systems, 22(1):7-22.
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD, DMKD ’03, page 2-11, New York, NY, USA.
Romani, L. A. S., de Avila, A. M. H., Zullo, J., Chbeir, R., Traina, C., and Traina, A. J. M. (2010). Clearminer: a new algorithm for mining association patterns on heterogeneous time series from climate data. In ACM, SAC ’10, page 900-905, New York, NY, USA.
Segura-Delgado, A., Gacto, M. J., Alcalá, R., and Alcalá-Fdez, J. (2020). Temporal association rule mining: An overview considering the time variable as an integral or implied component. WIREs Data Mining and Knowledge Discovery, 10(4):e1367.
Zhao, Y. and Zhang, T. (2017). Discovery of temporal association rules in multivariate time series. In International Conference on Mathematics, Modelling and Simulation Technologies and Applications, 2017, Xiamen, pages 294-300.
Amaral, T. and Sousa, E. (2019). Trier: A fast and scalable method for mining temporal exception rules. In Anais do XXXIV SBBD, pages 1-12. SBC.
Chen, X. and Petrounias, I. (2000). Discovering temporal association rules: Algorithms, language and system. In 16th ICDE, pages 306-306. IEEE.
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., and Smyth, P. (1998). Rule discovery from time series. In 4th ACM KDD, volume 98, pages 16-22.
de Oliveira, F. A., Costa, R. L., Goldschmidt, R. R., and Cavalcanti, M. C. (2017). Mineração de regras de associação multirrelação em grafos: Direcionando o processo de busca. In SBBD (Short Papers), pages 270-275.
Han, J., Kamber, M., and Pei, J. (2011). Data mining: Concepts and techniques. (3rd ed), Morgan Kauffman.
Harms, S. K. and Deogun, J. S. (2004). Sequential association rule mining with time lags. Journal of Intelligent Information Systems, 22(1):7-22.
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD, DMKD ’03, page 2-11, New York, NY, USA.
Romani, L. A. S., de Avila, A. M. H., Zullo, J., Chbeir, R., Traina, C., and Traina, A. J. M. (2010). Clearminer: a new algorithm for mining association patterns on heterogeneous time series from climate data. In ACM, SAC ’10, page 900-905, New York, NY, USA.
Segura-Delgado, A., Gacto, M. J., Alcalá, R., and Alcalá-Fdez, J. (2020). Temporal association rule mining: An overview considering the time variable as an integral or implied component. WIREs Data Mining and Knowledge Discovery, 10(4):e1367.
Zhao, Y. and Zhang, T. (2017). Discovery of temporal association rules in multivariate time series. In International Conference on Mathematics, Modelling and Simulation Technologies and Applications, 2017, Xiamen, pages 294-300.
Published
2022-09-19
How to Cite
KARASAWA, Eliane; SOUSA, Elaine P. M..
TRUMiner: Temporal Rules Mining in Multivariate and Heterogeneous Series Databases. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 403-408.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2022.226199.
