G-2008-13
Using Heuristics to Speed Up Frequent Pattern Mining
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In this paper we present a simple technique that uses background information to improve mining the frequent patterns of structured data. This technique uses a heuristic function that remaps the search space in a way the greatly reduces the number of costly subgraph isomorphism tests, without using space-expensive data structures. We illustrate our approach on a popular structured data mining problem, called the frequent subgraph mining problem, and show, through experiments on synthetic and real-life data, that this simple approach has advantages over other frequent pattern mining algorithms.
Paru en février 2008 , 17 pages
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jan. 2011
Improving constrained pattern mining with first-fail-based heuristics
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Data Mining and Knowledge Discovery, 23(1), 63–90, 2011
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