MBiS: an efficient method for mining frequent weighted utility itemsets from quantitative databases

Nguyen Duy Ham; Võ Đình Bảy; Nguyen Thi Hong Minh; Tzung-Pei Hong

doi:10.15625/1813-9663/31/1/5154

MBiS: an efficient method for mining frequent weighted utility itemsets from quantitative databases

Nguyen Duy Ham, Võ Đình Bảy, Nguyen Thi Hong Minh, Tzung-Pei Hong

Author affiliations

Authors

Nguyen Duy Ham Department of Math & Informatics, University of People’s Security, Hochiminh City, Vietnam
Võ Đình Bảy Faculty of Information Technology, Hochiminh City University of Technology, Vietnam
Nguyen Thi Hong Minh School of Graduate Studies, Vietnam National University, Hanoi, Vietnam
Tzung-Pei Hong Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC

DOI:

https://doi.org/10.15625/1813-9663/31/1/5154

Keywords:

Dynamic bit vector, frequent itemset, frequent weighted utility itemset, multi-bit segment, tidset.

Abstract

In recent years, methods for mining quantitative databases have been proposed. However, the processing time is fairly much, which affects the productivity of intelligent systems in the use of quantitative databases. This study proposes the multi-bit segment (MBiS) structure to store and process tidsets to increase the effeciency of mining frequent weighted utility itemsets (FWUIs) from quantitative databases. With this structure, the calculation of the intersection of tidsets between two itemsets becomes more convenient. Based on this structure, the authors define the MBiS-Tree structure and propose an algorithm for mining FWUIs from quantitative databases. Experimental results for a number of databases show that the proposed method outperforms existing methods.

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Published

16-03-2015

How to Cite

[1]

N. D. Ham, V. Đình Bảy, N. T. H. Minh, and T.-P. Hong, “MBiS: an efficient method for mining frequent weighted utility itemsets from quantitative databases”, JCC, vol. 31, no. 1, pp. 17–30, Mar. 2015.

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Issue

Vol. 31 No. 1 (2015)

Section

Computer Science

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