Affiliations: [a] University Clermont Auvergne, LIMOS, CNRS, UMR, France | [b] Digital Science CERI, IMT Lille Douai, University of Lille, Douai, France | [c] Department of Computer Engineering, ENSET, University of Douala, Cameroon
Correspondence:
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Corresponding author: Jerry Lonlac, Digital Science CERI, IMT Lille Douai, University of Lille, 59500 Douai, France. E-mail: jerry.lonlac@imt-lille-douai.fr.
Abstract: Mining frequent simultaneous attribute co-variations in numerical databases is also called frequent gradual pattern problem. Few efficient algorithms for automatically extracting such patterns have been reported in the literature. Their main difference resides in the variation semantics used. However in applications with temporal order relations, those algorithms fail to generate correct frequent gradual patterns as they do not take this temporal constraint into account in the mining process. In this paper, we propose an approach for extracting frequent gradual patterns for which the ordering of supporting objects matches the temporal order. This approach considerably reduces the number of gradual patterns within an ordered data set. The experimental results show the benefits of our approach.
