Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Time series similarity measures and time series indexing (abstract only)
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
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A time-profiled association is an association pattern consistent with a query sequence over time, e.g., identifying the interacting relationship of droughts and wild fires in Australia with the El Nino phenomenon in the past 50 years. Traditional association rule mining approaches reveal the generic dependency among variables in association patterns but do not capture the evolution of these patterns over time. Incorporating the temporal evolution of association patterns and identifying the co-occurring patterns consistent over time can be done by time-profiled association mining. Mining time-profiled associations is computationally challenging due to the large size of the itemset space and the long time points in practice. In this paper, we propose a novel one-step algorithm to unify the generation of statistical parameter sequences and sequence retrieval. The proposed algorithm substantially reduces the itemset search space by pruning candidate itemsets based on the monotone property of the lower bounding measure of the sequence of statistical parameters. Experimental results show that our algorithm outperforms a naive approach.