Abstract
Gradual patterns highlight complex order correlations of the form “The more/less X, the more/less Y”. Only recently algorithms have appeared to mine efficiently gradual rules. However, due to the complexity of mining gradual rules, these algorithms cannot yet scale on huge real world datasets. In this paper, we propose to exploit parallelism in order to enhance the performances of the fastest existing one (GRITE). Through a detailed experimental study, we show that our parallel algorithm scales very well with the number of cores available.
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Laurent, A., Negrevergne, B., Sicard, N., Termier, A. (2010). PGP-mc: Towards a Multicore Parallel Approach for Mining Gradual Patterns. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12026-8_8
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DOI: https://doi.org/10.1007/978-3-642-12026-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12025-1
Online ISBN: 978-3-642-12026-8
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