文章基本信息

标题：Mining Frequent Ranges of Numeric Attributes via Ant Colony Optimization for Continuous Domain without Specifying Minimum Support
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作者：Parisa Moslehi ; Behrouz Minaei ; Mahdi Nasiri 等
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：5
出版社：IJCSI Press
摘要：Currently, all search algorithms which use discretization of numeric attributes for numeric association rule mining, work in the way that the original distribution of the numeric attributes will be lost. This issue leads to loss of information, so that the association rules which are generated through this process are not precise and accurate. Based on this fact, algorithms which can natively handle numeric attributes of a dataset would be interesting. In this paper a new approach to finding frequent intervals of numeric attributes is presented using Ant Colony Optimization for Continuous domains (ACOR). The results show that this approach leads to more precise and accurate intervals in comparison with other approaches like discretizing into intervals with equal lengths.
关键词：Numeric Association Rule Mining; Preprocessing; Ant Colony Optimization; Data Mining