| 一种改进的基于密度的离群数据挖掘算法 |
| |
| 引用本文： | 崔贯勋朱庆生. 一种改进的基于密度的离群数据挖掘算法[J]. 计算机应用, 2007, 27(3): 559-561 |
| |
| 作者姓名： | 崔贯勋朱庆生 |
| |
| 作者单位： | 重庆大学,计算机学院,重庆,400044;重庆工学院,计算机科学与工程学院,重庆,400050;重庆大学,计算机学院,重庆,400044 |
| |
| 基金项目： | 国家自然科学基金 , 重庆市自然科学基金 |
| |
| 摘 要： | 利用基于密度的离群数据挖掘算法离群数据不在非离群数据指定的邻域内的特点，改进了原有的离群数据挖掘算法：首先判断数据是否在某个非离群数据指定的邻域内，如果不在，再判断其邻域内数据的个数。通过对二维空间数据测试表明，改进的算法能够快速有效地挖掘出数据集中的离群数据，速度上数倍于原来的算法。
|
| 关 键 词： | 数据挖掘 离群数据 基于密度 |
| 文章编号： | 1001-9081（2007）03-0559-02 |
| 收稿时间： | 2006-09-06 |
| 修稿时间： | 2006-09-052006-11-20 |
An improved density-based outlier mining algorithm |
| |
| CUI Guan-xun,ZHU Qing-sheng. An improved density-based outlier mining algorithm[J]. Journal of Computer Applications, 2007, 27(3): 559-561 |
| |
| Authors: | CUI Guan-xun ZHU Qing-sheng |
| |
| Affiliation: | 1. College of Computer Science, Chongqing University, Chongqing 400044, China; 2. School of Computer Science and Engineering, Chongqing Institute of Technology, Chongqing 400050, China |
| |
| Abstract: | Based on the characteristic that outliers are not included in the appointed neighborhood of inliers, an improved algorithm for outlier mining was proposed. Data was judged whether it was included in the appointed neighborhood of inliers firstly. If the answer was negative, the number of data that was included in the appointed neighborhood was counted. Experimental results show that the improved algorithm is effective and efficient in outlier mining and it is faster than the original algorithm. |
| |
| Keywords: | data mining outlier density-based |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机应用》浏览原始摘要信息 |
|
点击此处可从《计算机应用》下载全文 |
|
