Sampling Large Databases for Association Rules

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Sampling Large Databases for Association Rules (1996)

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by Hannu Toivonen

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460 - 3 self

BibTeX

@INPROCEEDINGS{Toivonen96samplinglarge,
    author = {Hannu Toivonen},
    title = {Sampling Large Databases for Association Rules},
    booktitle = {},
    year = {1996},
    pages = {134--145},
    publisher = {Morgan Kaufmann}
}

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Abstract

Discovery of association rules is an important database mining problem. Current algorithms for nding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very signi cant for very large databases. We present new algorithms that reduce the database activity considerably. Theidea is to pick a random sample, to ndusingthis sample all association rules that probably hold in the whole database, and then to verify the results with the restofthe database. The algorithms thus produce exact association rules, not approximations based on a sample. The approach is, however, probabilistic, and inthose rare cases where our sampling method does not produce all association rules, the missing rules can be found inasecond pass. Our experiments show that the proposed algorithms can nd association rules very e ciently in only onedatabase pass. 1

Keyphrases

association rule large database database activity exact association rule several pass onedatabase pas rare case analyzed database random sample restofthe database signi cant current algorithm important database mining problem present new algorithm whole database inasecond pas

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