Abstract
Mining gradual patterns plays a crucial role in many real world applications where huge volumes of complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. 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 thus propose to exploit parallelism in order to enhance the performances of the fastest existing one (GRITE) on multicore processors. 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., Négrevergne, B., Sicard, N., Termier, A. (2012). Efficient Parallel Mining of Gradual Patterns on Multicore Processors. In: Guillet, F., Ritschard, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25838-1_8
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DOI: https://doi.org/10.1007/978-3-642-25838-1_8
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