A Low-Scan Incremental Association Rule Maintenance Method Based on the Apriori Property View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2001-05-16

AUTHORS

Zequn Zhou , C.I. Ezeife

ABSTRACT

As new transactions update data sources and subsequently the data warehouse, the previously discovered association rules in the old database may no longer be interesting rules in the new database. Furthermore, some new interesting rules may appear in the new database. This paper presents a new algorithm for efficiently maintaining discovered association rules in the updated database, which starts by computing the high n level large itemsets in the new database using the available high n level large itemsets in the old database. Some parts of the n-1; n-2,...,1 level large itemsets can then be quickly generated by applying the apriori property, thereby avoiding the overhead of calculating many lower level large itemsets that involve huge table scans. More... »

PAGES

26-35

Book

TITLE

Advances in Artifical Intelligence

ISBN

978-3-540-61291-9
978-3-540-68450-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-45153-6_3

DOI

http://dx.doi.org/10.1007/3-540-45153-6_3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041583320


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