Abstract
In our previous approach, we proposed an apriori-based algorithm for mining highly coherent association rules, and it is time-consuming. In this paper, we present an efficient mining approach, which is a projection-based technique, to speed up the execution of finding highly coherent association rules. In particular, an indexing mechanism is designed to help find relevant transactions quickly from a set of data, and a pruning strategy is proposed as well to prune unpromising candidate itemsets early in mining. The experimental results show that the proposed algorithm outperforms the traditional mining approach for a real dataset.
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Chen, CH., Lan, GC., Hong, TP., Wang, SL., Lin, YK. (2014). A Projection-Based Approach for Mining Highly Coherent Association Rules. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_8
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DOI: https://doi.org/10.1007/978-3-319-07776-5_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07775-8
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