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A Comparative Study on Transaction Database Mining Algorithms

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dc.contributor.author Soe, Hay Mar
dc.contributor.author Kham, Nang Saing Moon
dc.date.accessioned 2019-07-15T07:49:56Z
dc.date.available 2019-07-15T07:49:56Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/898
dc.description.abstract Association Rules is the process of identifying relationships among set of items in transaction database. Finding frequent itemsets is the most expensive step in Association rule discovery. Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach is not appropriate. In this paper, mining association rules for Transaction database using FP-Growth and DynFP-Growth algorithms is presented. FP-Growth algorithm avoids or reduces candidate generation. Moreover, it greatly reduces the need to traverse the database. DynFP-Growth algorithm works in the same process as FP-Growth with lexicographic order, and stores at least one item is detected. Although resulting FPTree is too large to store in memory, it is more flexible with different support values. For the empirical study, three synthetic datasets by IBM Quest data generator is used: (a) T10I4D100K (b) T40I10D30K, and (c) T40I10D100K. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.title A Comparative Study on Transaction Database Mining Algorithms en_US
dc.type Article en_US

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