Mining Temporal Exception Rules from Multivariate Time Series Using a new Support Measure
DOI:
https://doi.org/10.5753/jidm.2020.2020Keywords:
Association Rule, Data Mining, Exception Rule, Time SeriesAbstract
Association rules are a common task to discover useful and comprehensive relationships among frequent and infrequent data. Frequent patterns describe a usual behavior, while infrequent ones represent uncommon knowledge. Our interest lies in finding exception rules, a class of infrequent patterns that may have critical effects as a consequence. Existing approaches for exception rules mining usually handle “itemsets databases”, where transactions are organized with no temporal information. However, temporality may be inherent to some real contexts and should be considered to improve the semantic quality of results. Moreover, these approaches implement a non-discriminatory support measure to estimate the relevance of an item, thus interpreting a large volume of data that may be merely occasional as patterns. Aiming to overcome these drawbacks, we propose TRiER (TempoRal Exception Ruler), an efficient method for mining temporal exception rules that not only discover exceptional behaviors and their causative agents, but also identifies how long consequences take to appear. We also present a new support measure to manipulate time series. This measure considers the context in which a pattern occurs, thus incorporating more semantics to the results obtained. We performed an extensive experimental analysis in real multivariate time series to verify the practical applicability of TRiER. Our results show TRiER has lower computational cost and is more scalable than existing approaches while finding a succinct and relevant set of patterns.
Downloads
References
Agrawal, R., Imielinski, T., and Swami, A. Mining association rules between sets of items in large databases. In Proceedings of International Conference on Management of Data. ACM Press, New York, NY, USA, pp. 207–216, 1993.
Agrawal, R. and Srikant, R. Mining sequential patterns. In Proceedings of ICDE. pp. 3–14, 1995.
Berzal, F., Blanco, I., Sánchez, D., and Vila, M. Measuring the accuracy and interest of association rules: a new framework. Intelligent Data Analysis, 2002.
Calvo-Flores, M., Ruiz, M., and Sánchez, D. New approaches for discovering exception and anomalous rules. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems vol. 19, pp. 361–399, 04, 2011.
Casanova, I. J., Campos, M., Juarez, J., Fernandez-Fernandez-Arroyo, A., and Lorente, J. Impact of time series discretization on intensive care burn unit survival classification. Progress in Artificial Intelligence, 2017.
Daly, O. and Taniar, D. Exception rules in data mining. Applied Mathematics and Computation, 2008.
Dong, G. Sequence data mining. Springer-Verlag, Berlin, Germany, 2009.
Gan, W., Lin, J. C., Fournier-Viger, P., Chao, H., and Yu, P. S. A survey of parallel sequential pattern mining. Transactions on Knowledge Discovery from Data, 2019.
Hussain, F., Liu, H., Suzuki, E., and Lu, H. Exception rule mining with a relative interestingness measure. In Proceedings of Knowledge Discovery and Data Mining. ACM Press, New York, NY, USA, pp. 86–97, 2000.
Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, 2001.
Lin, J., Keogh, E., Wei, L., and Lonardi, S. Experiencing sax: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 2007.
Lucas, A. and Schuler, C. Análise do NDVI/NOAA em cana-de-açúcar na ata Atlântica no litoral norte de Pernambuco, Brasil. Revista Brasileira de Engenharia Agrícola e Ambiental, 2007.
Marcuzzo, F. F. N. and Romero, V. Influência do El Niño e La Niña na precipitação máxima diária do estado de Goiás. Revista Brasileira de Meteorologia, 2013.
Mitsa, T. Temporal data mining. Chapman & Hall/CRC, Minneapolis, U.S.A., 2010.
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.-C. Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of ICDE. pp. 215–224, 2001.
Ruiz, M. D., Sánchez, D., Delgado, M., and Martin-Bautista, M. J. Discovering fuzzy exception and anomalous rules. IEEE Transactions on Fuzzy Systems 24 (4): 930–944, 2016.
Srikant, R. and Agrawal, R. Mining sequential patterns: Generalizations and performance improvements. In Advances in Database Technology — EDBT ’96, P. Apers, M. Bouzeghoub, and G. Gardarin (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 1–17, 1996.
Suzuki, E. Discovering unexpected exceptions: a stochastic approach. In Proceedings of Rough Sets, Fuzzy Sets, and Machine Discovery. Tokyo University Press, Tokyo, pp. 259–262, 1996.
Zaki, M. J. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 2000.
Zaki, M. J. Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 2001.
