| 一种基于密度的不确定数据离群点检测算法 |
| |
| 引用本文： | 姜元凯,郑洪源,丁秋林. 一种基于密度的不确定数据离群点检测算法[J]. 计算机科学, 2015, 42(4): 172-176 |
| |
| 作者姓名： | 姜元凯 郑洪源 丁秋林 |
| |
| 作者单位： | 南京航空航天大学计算机科学与技术学院 南京210016 |
| |
| 基金项目： | 本文受江苏省产学研联合创新资金项目(SBY201320423)资助 |
| |
| 摘 要： | 不确定数据普遍存在于如移动计算、RFID技术和传感器网络等大量应用之中.由于不确定数据的离群点检测算法可以提高服务质量,提出一种基于密度的不确定数据离群检测算法RLOF.该算法引入一种R2-tree结构,有效降低了计算局部离群因子时的时间复杂度,同时降低了不确定数据集中的数据更新成本以及海量数据维护成本.理论分析和实验结果充分证明了该算法是有效可行的.
|
| 关 键 词： | 不确定数据 离群点检测 R2-tree索引 最小充分邻域 |
On Density Based Outlier Detection for Uncertain Data |
| |
| JIANG Yuan-kai,ZHENG Hong-yuan and DING Qiu-lin. On Density Based Outlier Detection for Uncertain Data[J]. Computer Science, 2015, 42(4): 172-176 |
| |
| Authors: | JIANG Yuan-kai ZHENG Hong-yuan DING Qiu-lin |
| |
| Affiliation: | College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China,College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China and College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China |
| |
| Abstract: | Uncertain data generally exist in a large number of applications,such as mobile computing,sensor networks and RFID technology.Outliers detection algorithm can improve the quality of these services.An uncertain data outlier detection algorithm based on density RLOF was proposed.This algorithm introduces a R2-tree structure,which effectively reduces the time complexity when calculating local outlier factor.It also reduces the cost of data updating in the uncertain data set and the maintenance cost of a massive data.The theoretical analysis and experimental results fully prove that the algorithm is effective and feasible. |
| |
| Keywords: | Uncertain data Outlier detection R2-tree index Minimal sufficient neighborhood |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
|
点击此处可从《计算机科学》下载全文 |
