Improved BVBUC algorithm to discover closed itemsets in long biological datasets

Md Zaki, Fatimah Audah and Zulkurnain, Nurul Fariza (2019) Improved BVBUC algorithm to discover closed itemsets in long biological datasets. Applied Mechanics and Materials, 892. pp. 157-167. ISSN 1662-7482

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Abstract

The task in mining closed frequent itemsets requires the algorithm to mine the frequent ones then determine its closure. The efficiency of closure computation is very important as it will determine the total mining time and the required memory. Over the years, many closure computation methods have been proposed to achieve these goals. However, to the best of our knowledge, there is no suitable method that can be adapted for algorithms that enumerate the rowset lattice, which is effective for biological datasets. Therefore, this paper proposed a method for computing closure compare with the method used in BVBUC algorithm method. Finally, BVBUC_I is proposed and the performances of these algorithms were evaluated using two synthetic datasets and three real datasets. The results of these tests proved the efficiency of the proposed method.

Item Type:	Article (Journal)
Additional Information:	4123/79195
Uncontrolled Keywords:	Data mining, association rules, closed itemset, bioinformatics.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	DR Nurul Fariza Zulkurnain
Date Deposited:	10 Mar 2020 16:56
Last Modified:	10 Mar 2020 16:56
URI:	http://irep.iium.edu.my/id/eprint/79195

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