A Data Stream Outlier Detection Algorithm Based on Grid

被引：0

作者：

Yu Xiang ^{[1
]}

Lei Guohua ^{[1
]}

Xu Xiandong ^{[1
]}

Lin Liandong ^{[2
]}

机构：

[1] Heilongjiang Inst Technol, Coll Comp Sci & Technol, Harbin 150050, Peoples R China

[2] Heilongjiang Univ, Coll Elect Engn, Harbin 150080, Peoples R China

来源：

2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2015年

关键词：

data mining; data stream; outlier detection; grid;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The main aim of data stream outlier detection is to find the data stream outliers in rational time accurately. The existing outlier detection algorithms can find outliers in static data sets efficiently, but they are inapplicable for the dynamic data stream, and cannot find the abnormal data effectively. Due to the requirements of real-time detection, dynamic adjustment and the inapplicability of existing algorithms on data stream outlier detection, we propose a new data stream outlier detection algorithm, ODGrid, which can find the abnormal data in data stream in real time and adjust the detection results dynamically. According to the experiments on real datasets and synthetic datasets, ODGrid is superior to the existing data stream outlier detection algorithms, and it has good scalability to the dimensionality of data space.

引用

页码：4136 / 4141

页数：6

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