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Outlier Detection Technique in Data Mining: A Research Perspective

机译:数据挖掘中的异常检测技术:研究视角

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摘要

While the field of data mining has been studied extensively, most of the work has concentrated on discovery of patterns. Outlier detection as a branch of data mining has many important applications, and deserves more attention from data mining community. Most methods in the early work that detects outliers independently have been developed in field of Statistics. Finding ,removing and detecting outliers is very important in data mining, for example error in large databases can be extremely common, so an important property of a data mining algorithm is robustness with respect to outliers in the database. Most sophisticated methods in data mining address this problem to some extent, but not fully, and can be improved by addressing the problem more directly. The identification of outliers can lead to the discovery of unexpected knowledge in areas such as credit card fraud detection, calling card fraud detection, discovering criminal behaviors, discovering computer intrusion, etc. In this paper we will explain the first part of our research, which is focused on outlier identification and provide a description of why an identified outlier exceptional, based on Distance-Based outlier detection and Density-Based outlier detection.
机译:尽管对数据挖掘领域进行了广泛的研究,但大多数工作都集中在发现模式上。异常检测作为数据挖掘的一个分支具有许多重要的应用,应引起数据挖掘社区的更多关注。在早期工作中,大多数独立检测异常值的方法都是在统计领域开发的。查找,删除和检测离群值在数据挖掘中非常重要,例如大型数据库中的错误可能非常普遍,因此,数据挖掘算法的重要属性是针对数据库中的离群值的鲁棒性。数据挖掘中最复杂的方法在某种程度上但并非完全解决了这个问题,可以通过更直接地解决此问题来加以改进。异常值的识别可以导致在信用卡欺诈检测,电话卡欺诈检测,发现犯罪行为,发现计算机入侵等领域发现意外知识。在本文中,我们将解释研究的第一部分,即本文将重点介绍离群值识别,并基于基于距离的离群值检测和基于密度的离群值检测,说明为什么识别出的离群值异常。

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  • 年度 2005
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