Outlier detection algorithms in data mining systems

被引：46
|
作者：
Petrovskiy, MI [1 ]
机构：
[1] Moscow MV Lomonosov State Univ, Dept Computat Math & Cybernet, Moscow 119992, Russia
来源：
PROGRAMMING AND COMPUTER SOFTWARE | 2003年 / 29卷 / 04期
基金：
俄罗斯基础研究基金会;
关键词：
Algorithms - Computer operating systems - Fuzzy sets - Numerical methods - Problem solving - Protection;
D O I：
10.1023/A:1024974810270
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The paper discusses outlier detection algorithms used in data mining systems. Basic approaches currently used for solving this problem are considered, and their advantages and disadvantages are discussed. A new outlier detection algorithm is suggested. It is based on methods of fuzzy set theory and the use of kernel functions and possesses a number ofadvantages compared to the existing methods. The performance of' the algorithm suggested is studied by the example of the applied problem of anomaly detection arising in computer protection systems, the so-called intrusion detection systems.
引用
收藏
页码：228 / 237
页数：10
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