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http://dx.doi.org/10.3745/KIPSTD.2006.13D.3.293

Frequent Itemset Creation using Bit Transaction Clustering in Data Mining  

Kim Eui-Chan (가톨릭대학교 컴퓨터공학과)
Hwang Byung-Yeon (가톨릭대학교 컴퓨터정보공학부)
Publication Information
The KIPS Transactions:PartD / v.13D, no.3, 2006 , pp. 293-298 More about this Journal
Abstract
Many data are stored in database. For getting any information from many data, we use the query sentences. These information is basic and simple. Data mining method is various. In this paper, we manage clustering and association rules. We present a method for finding the better association rules, and we solve a problem of the existing association rules. We propose and apply a new clustering method to fit for association rules. It is not clustering of the existing distance basis or category basis. If we find association rules of each clusters, we can get not only existing rules found in all transaction but also rules that will be characteristics of clusters. Through this study, we can expect that we will reduce the number of many transaction access in large databases and find association of small group.
Keywords
Data Mining; Bit Transaction; Clustering; Association Rules;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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