| 基于粗糙集的高维空间离群点发现算法研究 |
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| 引用本文： | 许龙飞,熊君丽.基于粗糙集的高维空间离群点发现算法研究[J].计算机工程与应用,2004,40(7):58-60,77. |
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| 作者姓名： | 许龙飞 熊君丽 |
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| 作者单位： | 暨南大学信息科技学院计算机科学系,广州,510632 |
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| 基金项目： | 北京大学视听觉信息处理国家重点实验室基金资助 |
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| 摘 要： | 该文提出在高维空间下离群点发现技术的新方法,即利用粗糙集的属性约简技术减少高维空间的维数,并在各个关联规则子空间下对数据集进行基于密度的离群点挖掘,使高维空间下的离群点挖掘更具有实用性。数据分析表明,该算法能有效地发现高维空间数据集中的离群点。
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| 关 键 词： | 粗糙集 属性约简 基于密度 离群点发现 |
| 文章编号： | 1002-8331-(2004)07-0058-03 |
Study on Algorithm for Rough Set-Based Outlier Detection in High Dimension Space |
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| Xu,Longfei Xiong Junli.Study on Algorithm for Rough Set-Based Outlier Detection in High Dimension Space[J].Computer Engineering and Applications,2004,40(7):58-60,77. |
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| Authors: | Xu Longfei Xiong Junli |
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| Abstract: | In this paper,a new method on outlier detection technique in high-dimensional space is presented,which uses reduction attributes character of rough set to cut out some inessential attributes and detects outliers density-based in subspace of every association rule.Then the outlier detection technique in high-dimensional space is more useful.The analysis of data shows that this algorithm can find the outliers in high-dimensional space effectively. |
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| Keywords: | Rough set Attribute reduction Density-based Outlier detection |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
