English  |  正體中文  |  简体中文  |  Items with full text/Total items : 28611/40649
Visitors : 638178      Online Users : 84
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52574

Title: Mining diversified association rules in big datasets: A cluster/GPU/genetic approach
Authors: Youcef Djenouri
Asma Belhadi
Philippe Fournier-Viger
Hamido Fujita
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Association rule mining
GPU-based algorithm
Genetic algorithm
Cluster of GPUs
Date: 2018-08
Issue Date: 2019-11-19T01:09:49Z
Publisher: Information Sciences
Abstract: Abstract: Association rule mining is a popular data mining task, which has important in many do- mains. Because the task of association rule mining is very time consuming, evolutionary and swarm based algorithms have been designed to find approximate solutions. However, these approaches still have long execution times, especially when applied on dense and big databases, or when low minsup and minconf threshold values are used. Moreover, these approaches suffer from the lack of diversity in the rules presented to the user. To address these drawbacks of previous algorithms, this paper proposes an efficient parallel algorithm named CGPUGA. It is a genetic algorithm that runs on clusters of GPUs to efficiently dis- cover diversified association rules. It benefits from cluster computing to generate rules. Then, to evaluate rules, which is the most time consuming task, the designed algorithm relies on the massively parallel GPU threads. Furthermore, to deal with the issue of rule quality, the search space of rules is partitioned into several regions assigned to different workers, and rules found by each workers are the merged to ensure diversification. The designed approach has been empirically compared with state-of-the-art algorithms using small, medium, large and big datasets. Results reveal that CGPUGA is 600 times faster than the sequential version of the algorithm for big datasets. Moreover, it outperforms state-of- the-art high performance computing based association rule mining algorithms for real big datasets such as Pokec, Webdo cs and Wikilinks. In terms of rule quality, results show that the designed CGPUGA algorithm provides rules of higher quality compared to the state-of- the-art NIGGAR, MSP-MPSO and MPGA algorithms for diversified association rule mining.
Relation: 459 pp.117-134
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52574
Appears in Collections:[資訊工程學系] 期刊論文

Files in This Item:

File Description SizeFormat

All items in NTOUR are protected by copyright, with all rights reserved.


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback