UCSY's Research Repository

Improving Association Rules Mining by Hashing Algorithm

Show simple item record

dc.contributor.author Lwin, Nang Khine Zar
dc.contributor.author Tun, Nay Min
dc.date.accessioned 2019-07-22T04:24:43Z
dc.date.available 2019-07-22T04:24:43Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1114
dc.description.abstract Association rule mining, is one of the most important and well researched techniques of data mining. It is the process of discovering large itemsets appeared in a sufficient number of transactions. Large itemsets from a huge number of candidate large itemsets are dominating factor for the overall data mining performance. This paper presents mining association rules among items in a large database of sales transactions. To address this problem, it present an effective hash-based algorithm for the candidate set generation. This system applies an algorithm DHP (Direct Hashing and Pruning) on application cosmetic sales data to generate frequent association patterns. Generation of smaller candidate sets enables to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost of later iterations significantly. The experimental results of our system are also discussed in this paper. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.title Improving Association Rules Mining by Hashing Algorithm en_US
dc.type Article en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Repository


My Account