Formal concept analysis has been successfully applied as a data mining framework whereby target patterns come in the form of intent families and implication bases. Since their extraction is a challenging task, especially for large datasets, parallel techniques should be helpful in reducing the computational effort and increasing the scalability of the approach. In this paper we describe a way to parallelize a recent divide-and-conquer method computing both the intents and the Duquenne-Guiges implication basis of dataset. Wile intents admit a straightforward computation, adding the basis - whose definition is recursive - poses harder problems, in particular, for parallel design. A first, and by no means final, solution relies on a partition of the basis that allows the crucial and inherently sequential step of redundancy removal to be nevertheless split into parallel subtasks. A prototype implementation of our method, called PARCIM, shows a nearly linear acceleration w.r.t. its sequential counter-part. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Kengue, J. F. D., Valtchev, P., & Djamegni, C. T. (2007). Parallel computation of closed itemsets and implication rule bases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4742 LNCS, pp. 359–370). Springer Verlag. https://doi.org/10.1007/978-3-540-74742-0_34
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