An Efficient Algorithm for Mining Association Rules in Large Databases

dc.contributor.author Omiecinski, Edward
dc.contributor.author Navathe, Shamkant B.
dc.contributor.author Savasere, Ashok en_US
dc.date.accessioned 2005-06-17T17:54:28Z
dc.date.available 2005-06-17T17:54:28Z
dc.date.issued 1995 en_US
dc.description.abstract Mining for association rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an efficient algorithm for mining association rules that is fundamentally different from known algorithms. Compared to the previous algorithms, our algorithm reduces both CPU and I/O overheads. In our experimental study it was found that for large databases, the CPU overhead was reduced by as much as a factor of seven and I/O was reduced by almost an order of magnitude. Hence this algorithm is especially suitable for very large size databases. The algorithm is also ideally suited for parallelization. We have performed extensive experiments and compared the performance of the algorithm with one of the best existing algorithms. en_US
dc.format.extent 291826 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/6678
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries CC Technical Report; GIT-CC-95-04 en_US
dc.subject Association rules
dc.subject Database mining
dc.subject CPU overheads
dc.subject I/O overheads
dc.subject Parallelization
dc.subject Large databases
dc.subject Algorithms
dc.title An Efficient Algorithm for Mining Association Rules in Large Databases en_US
dc.type Text
dc.type.genre Technical Report
dspace.entity.type Publication
local.contributor.author Navathe, Shamkant B.
local.contributor.corporatename College of Computing
local.relation.ispartofseries College of Computing Technical Report Series
relation.isAuthorOfPublication 9a3ecea2-fb35-40ed-adc3-4d1802a4ddcf
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication 35c9e8fc-dd67-4201-b1d5-016381ef65b8
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