| 基于关联分析的高维空间异常点发现 |
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| 引用本文： | 陆介平,倪巍伟,孙志挥.基于关联分析的高维空间异常点发现[J].应用科学学报,2006,24(1):60-63. |
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| 作者姓名： | 陆介平 倪巍伟 孙志挥 |
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| 作者单位： | 东南大学计算机科学与工程系,江苏,南京,210096 |
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| 基金项目： | 中国科学院资助项目;中南大学校科研和教改项目;国家自然科学基金;科技部科技型中小企业技术创新项目 |
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| 摘 要： | 异常点发现是从大量数据对象中挖掘少量具有异常行为模式的数据对象,很多情况下,这些数据对象较之正常行为模式包含了更多用户感兴趣的信息.该文针对某些具体应用领域中的数据对象具有高维性的特点,利用关联分析知识,提出一种高维空间异常点发现算法,理论分析和实验表明,算法是有效可行的.
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| 关 键 词： | 异常点 关联规则 闭频繁项集 k关系邻域 |
| 文章编号： | 0255-8297（2006）01-0060-04 |
| 收稿时间： | 2004-09-27 |
| 修稿时间： | 2004-09-272004-12-14 |
Discovery of High Dimensional Outliers Based on Association Analysis |
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| LU Jie-ping,NI Wei-wei,SUN Zhi-hui.Discovery of High Dimensional Outliers Based on Association Analysis[J].Journal of Applied Sciences,2006,24(1):60-63. |
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| Authors: | LU Jie-ping NI Wei-wei SUN Zhi-hui |
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| Institution: | Department of Computer Science and Engineering, Southeast University, Nanfing 210096, China |
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| Abstract: | Discovery of outliers is to extract a few data objects with abnormal behavior patterns, which are more interesting than common patterns in some cases, from a large amount of data. It is of practical significance in intrusion detection systems, credit fraud detection, etc. Data in these domains are usually high dimensional, particularly featured by their sparseness and decline properties. An algorithm that can obtain the outliers with high efficiency is proposed based on association analysis. Effectiveness of the algorithm is shown by theory analysis and experiment results. |
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| Keywords: | outlier association analysis closed frequent item-sets k-relational neighboring area |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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