Mining frequent closed itemsets without candidate generation

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Abstract

Mining frequent closed itemsets provides complete and non-redundant result for the analysis of frequent pattern. Most of the previous studies adopted the FP-tree based conditional FP-tree generation and candidate itemsets generation-and-test approaches. However, those techniques are still costly, especially when there exists prolific and/or long itemsets. This paper redesigns FP-tree structure and proposes a novel algorithm based on it. This algorithm not only avoids building conditional FP-tree but also can get frequent closed itemsets directly without candidate itemsets generation. The experimental results show the advantage and improvement of these strategies. © Springer-Verlag Berlin Heidelberg 2005.

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Chen, K. (2005). Mining frequent closed itemsets without candidate generation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3758 LNCS, pp. 668–677). Springer Verlag. https://doi.org/10.1007/11576235_68

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