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Performance Testing on Two Methods of Mining Frequent Patterns from Dense Data

Norwati Mustapha, and Md Nasir Sulaiman, and Mohamed Othman, and Mohd Hasan Selamat, (2004) Performance Testing on Two Methods of Mining Frequent Patterns from Dense Data. In: Proceedings of the Joint Conference on Informatics and Research on Women in ICT (RWICT) 2004 , 28 - 30 July 2004, Putra World Trade Center, Kuala Lumpur, Malaysia .

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Affiliations

Universiti Putra Malaysia, Faculty of Computer Science and Information Technology
Universiti Putra Malaysia, Faculty of Computer Science and Information Technology
Universiti Putra Malaysia, Faculty of Computer Science and Information Technology
Universiti Putra Malaysia, Faculty of Computer Science and Information Technology

Abstract

Many algorithms of mining frequent patterns were developed to tackle datasets primarily from the domain of market-basket analysis, where the frequent patterns are very short There are some interests in applying these algorithms to other domains such as census data and telecommunication data that very dense. These kinds of datasets contain very long frequent patterns and very correlated data. This study is experimentally examined the two existing methods, Apriori and DIC for mining frequent patterns on dense data. The both methods are implemented and compared their performances of enumerating frequent patterns. An extensive experimental evaluation on d number of datasets shows that Apriori is outperformed DIC on the high support but DIC beat out Apriori for the lower support on the dense data. Strengths and weaknesses of these two methods are also discussed.

Item Type:Conference or Workshop Item (Paper)
Keywords:Data mining, frequent patterns, association rules
Subjects:Q Science, Computer Science
ID Code:1112

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