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主要題名:Application of particle swarm optimization to association rule mining
作者姓名:Kuo, R.-J.Chiu, Y.-T.Chao, Chuang-Min
貢獻者資料:管理學院/經營管理系
關鍵詞:association rule miningparticle swarm optimization algorithm
論文中文摘要:In the area of association rule mining, most previous research had focused on improving computational
efficiency. However, determination of the threshold values of support and confidence, which seriously
affect the quality of association rule mining, is still under investigation. Thus, this study intends to propose
a novel algorithm for association rule mining in order to improve computational efficiency as well
as to automatically determine suitable threshold values. The particle swarm optimization algorithm first
searches for the optimum fitness value of each particle and then finds corresponding support and confidence
as minimal threshold values after the data are transformed into binary values. The proposed
method is verified by applying the FoodMart2000 database of Microsoft SQL Server 2000 and compared
with a genetic algorithm. The results indicate that the particle swarm optimization algorithm really can
suggest suitable threshold values and obtain quality rules. In addition, a real-world stock market database
is employed to mine association rules to measure investment behavior and stock category purchasing.
The computational results are also very promising.
出版日期:2011-01
論文ID:11073-pa-cm-2011-01_1p
典藏單位:國立臺北科技大學
數位物件檔名:11073-pa-cm-2011-01_1p.pdf
統一資源識別號:http://dx.doi.org/10.1016/j.asoc.2009.11.023
備註:© 2009 Elsevier B.V. All rights reserved.
資料開放狀態:開放
刊物名稱:Applied Soft Computing
期數:11(1)
論文起迄頁碼:326-336