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Comparative Study of Association Rules Mining

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dc.contributor.author Aye, Thi Dar
dc.contributor.author Soe, Khin Mar
dc.date.accessioned 2019-07-15T08:01:40Z
dc.date.available 2019-07-15T08:01:40Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/900
dc.description.abstract Association rule mining is a technique to find useful patterns and associations in transactional databases. The mining of association rules can be mapped into the problem of discovering large (frequent) itemsets where is a grouped of items which appear in a sufficient number of transaction. The discovery of interesting association relationships among huge amount of business transaction records can help in many business decision making process . There are many association rules mining algorithms. But this system is intended to make the comparative study of three association rules mining algorithms such as DHP algorithm, PHP algorithm and Hybrid Approach of Support-Ordered Tree and PHP based on same dataset. Both DHP and PHP algorithm use hash base method and pruning method to reduce database size. DHP use direct hashing technique. PHP use perfect hashing technique. The two dataset, Kyar Nyo Pan Stationary Store and Orange minimarket are used. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.subject association rule en_US
dc.subject database en_US
dc.subject frequent pattern en_US
dc.subject itemset en_US
dc.title Comparative Study of Association Rules Mining en_US
dc.type Article en_US


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