Evolution and maintenance of frequent pattern space when transactions are removed
Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference …, 2007•Springer
This paper addresses the maintenance of discovered frequent patterns when a batch of
transactions are removed from the original dataset. We conduct an in-depth investigation on
how the frequent pattern space evolves under transaction removal updates using the
concept of equivalence classes. Inspired by the evolution analysis, an effective and exact
algorithm TRUM is proposed to maintain frequent patterns. Experimental results
demonstrate that our algorithm outperforms representative state-of-the-art algorithms.
transactions are removed from the original dataset. We conduct an in-depth investigation on
how the frequent pattern space evolves under transaction removal updates using the
concept of equivalence classes. Inspired by the evolution analysis, an effective and exact
algorithm TRUM is proposed to maintain frequent patterns. Experimental results
demonstrate that our algorithm outperforms representative state-of-the-art algorithms.
Abstract
This paper addresses the maintenance of discovered frequent patterns when a batch of transactions are removed from the original dataset. We conduct an in-depth investigation on how the frequent pattern space evolves under transaction removal updates using the concept of equivalence classes. Inspired by the evolution analysis, an effective and exact algorithm TRUM is proposed to maintain frequent patterns. Experimental results demonstrate that our algorithm outperforms representative state-of-the-art algorithms.
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