Abstract
We present MOWCATL, an efficient method for mining frequent sequential association rules from multiple sequential data sets with a time lag between the occurrence of an antecedent sequence and the corresponding consequent sequence. This approach finds patterns in one or more sequences that precede the occurrence of patterns in other sequences, with respect to user-specified constraints. In addition to the traditional frequency and support constraints in sequential data mining, this approach uses separate antecedent and consequent inclusion constraints. Moreover, separate antecedent and consequent maximum window widths are used to specify the antecedent and consequent patterns that are separated by the maximum time lag.
We use multiple time series drought risk management data to show that our approach can be effectively employed in real-life problems. The experimental results validate the superior performance of our method for efficiently finding relationships between global climatic episodes and local drought conditions. We also compare our new approach to existing methods and show how they complement each other to discover associations in a drought risk management decision support system.
This research was supported in part by NSF Digital Government Grant No. EIA-0091530 and NSF EPSCOR, Grant No. EPS-0091900.
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Harms, S.K., Deogun, J., Tadesse, T. (2002). Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_47
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DOI: https://doi.org/10.1007/3-540-48050-1_47
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