Abstract
Sequential rule mining is an important data mining task with wide applications. The current state-of-the-art algorithm (RuleGrowth) for this task relies on a pattern-growth approach to discover sequential rules. A drawback of this approach is that it repeatedly performs a costly database projection operation, which deteriorates performance for datasets containing dense or long sequences. In this paper, we address this issue by proposing an algorithm named ERMiner (Equivalence class based sequential Rule Miner) for mining sequential rules. It relies on the novel idea of searching using equivalence classes of rules having the same antecedent or consequent. Furthermore, it includes a data structure named SCM (Sparse Count Matrix) to prune the search space. An extensive experimental study with five real-life datasets shows that ERMiner is up to five times faster than RuleGrowth but consumes more memory.
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Fournier-Viger, P., Gueniche, T., Zida, S., Tseng, V.S. (2014). ERMiner: Sequential Rule Mining Using Equivalence Classes. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_10
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DOI: https://doi.org/10.1007/978-3-319-12571-8_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12570-1
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