Scalable Algorithms for Association Mining

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Scalable Algorithms for Association Mining (2000)

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by Mohammed J. Zaki

Venue:	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Citations:	259 - 23 self

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@ARTICLE{Zaki00scalablealgorithms,
    author = {Mohammed J. Zaki},
    title = {Scalable Algorithms for Association Mining},
    journal = {IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING},
    year = {2000},
    volume = {12},
    pages = {372--390}
}

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Abstract

Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. In this paper we present efficient algorithms for the discovery of frequent itemsets, which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The items are organized into a subset lattice search space, which is decomposed into small independent chunks or sub-lattices, which can be solved in memory. Efficient lattice traversal techniques are presented, which quickly identify all the long frequent itemsets, and their subsets if required. We also present the effect of using different database layout schemes combined with the proposed decomposition and traversal techniques. We experimentally compare the new algorithms against the previous approaches, obtaining ...

Keyphrases

association mining frequent itemsets scalable algorithm compute intensive phase association mining task structural property different database layout scheme conditional implication rule efficient lattice traversal technique small independent chunk previous approach new algorithm knowledge discovery important problem traversal technique data mining long frequent itemsets association rule discovery fast discovery present efficient algorithm subset lattice search space

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