Frequent Itemset Creation using Bit Transaction Clustering in Data Mining 


Vol. 13,  No. 3, pp. 293-298, Jun.  2006
10.3745/KIPSTD.2006.13.3.293


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  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.

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[IEEE Style]

E. C. Kim and B. Y. Hwang, "Frequent Itemset Creation using Bit Transaction Clustering in Data Mining," The KIPS Transactions:PartD, vol. 13, no. 3, pp. 293-298, 2006. DOI: 10.3745/KIPSTD.2006.13.3.293.

[ACM Style]

Eui Chan Kim and Byung Yeon Hwang. 2006. Frequent Itemset Creation using Bit Transaction Clustering in Data Mining. The KIPS Transactions:PartD, 13, 3, (2006), 293-298. DOI: 10.3745/KIPSTD.2006.13.3.293.

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