A celerity association rules method based on data sort search

Authors:
Zhiwei Huang;Qin Liao
Affiliations:
Guangzhou South China University of Technology;Guangzhou South China University of Technology
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

Citing 2
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Mining association rules between sets of items in large databases

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Abstract

Discovering frequent item sets is a key problem in data mining association rules. In this paper, there is a celerity association rules method based on data sort search. Using the plenitude and call terms of frequent item sets, the method efficiency can be improved greatly for the searching time won't increase as the number of item set of the data does, moreover the data can be found by searching the database within 3 times. Using the change between the frequent item sets and standby item sets, the data celerity renew and the min-sup renew can be true.