| 基于PCA及属性距离和的孤立点检测算法 |
| |
| 引用本文： | 张忠平,宋少英,宋晓辉.基于PCA及属性距离和的孤立点检测算法[J].计算机工程与应用,2009,45(17):139-141. |
| |
| 作者姓名： | 张忠平 宋少英 宋晓辉 |
| |
| 作者单位： | 燕山大学,信息科学与工程学院,河北,秦皇岛,066004 |
| |
| 基金项目： | 国家自然科学基金，教育部科学技术研究重点项目，河北省教育厅科研计划 |
| |
| 摘 要： | 提出了一种基于主分量分析和属性距离和的孤立点检测算法。该方法首先通过主分量分析方法从众多属性中提取出满足累计贡献率的主分量,同时利用PCA变换矩阵把原始数据集转换到由主分量组成的新的特征空间上,之后对转换后的数据集用属性距离和的方法对孤立点进行检测。实验结果证明了基于主分量分析和属性距离和的孤立点检测算法的有效性。
|
| 关 键 词： | 孤立点 主分量分析 累计贡献率 属性距离和 |
| 收稿时间： | 2008-4-2 |
| 修稿时间： | 2008-6-17
|
Algorithm for outlier detection based on principal component analysis and sum of attributes distance |
| |
| ZHANG Zhong-ping,SONG Shao-ying,SONG Xiao-hui.Algorithm for outlier detection based on principal component analysis and sum of attributes distance[J].Computer Engineering and Applications,2009,45(17):139-141. |
| |
| Authors: | ZHANG Zhong-ping SONG Shao-ying SONG Xiao-hui |
| |
| Affiliation: | College of Information Science and Engineering，Yanshan University，Qinhuangdao，Hebei 066004，China |
| |
| Abstract: | An outlier detection algorithm based on principal component analysis and the sum of attributes distance is proposed.The algorithm firstly extracts the principal components from many attributes satisfying accumulative contribution rate.Simultaneously，by the PCA matrix original dataset is transformed to a new feature space composed of principal component.Then outliers are detected using the approach of the sum of attributes distance in the transformed datasets.The results of the experiment show that the outlier detection algorithm based on principal component analysis and the sum of attributes distance is effective. |
| |
| Keywords: | outlier principal component analysis accumulative contribution rate the sum of attributes distance |
| 本文献已被 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
