FLDS: Fast Outlier Detection Based on Local Density Score

V. V. Thang, D. V. Pantiukhin, A. Nazarov
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引用次数: 3

Abstract

The problem of outlier detection is one of the issues attracting much attention from researchers in the field of data mining and knowledge discovery from data (KDD). Detecting outliers in a data set is to find different points than the majority of the remaining points. Currently, many studies have been introduced to address this problem. One of the widely used algorithms is based on the evaluation of local density of data, for example LDS or LOF algorithm, however, the drawback of these methods is that the high computational complexity cost O (n2). In this paper, we propose a method for outlier detection based on k-nearest neighbors with the complexity of the method is O (n1.5), named FLDS. The basic idea of this method is to use a K-Means algorithm to devide dataset into k clusters, then find the singularity in this clusters. Experimental results show that the new algorithm is capable of detecting outliers similar to LOF, but the calculation time is faster about 20 times than LOF.
基于局部密度评分的快速离群点检测
异常点检测问题是数据挖掘和从数据中发现知识(KDD)领域备受关注的问题之一。检测数据集中的异常值是找到与大多数剩余点不同的点。目前,已经有很多研究来解决这个问题。目前广泛使用的算法之一是基于数据局部密度的评估,如LDS或LOF算法,但这些方法的缺点是计算复杂度高,代价为O (n2)。本文提出了一种基于k近邻的离群点检测方法,该方法的复杂度为0 (n1.5),命名为FLDS。该方法的基本思想是使用k - means算法将数据集划分为k个聚类,然后在这些聚类中找到奇异点。实验结果表明,新算法能够检测出与LOF相似的异常点,但计算时间比LOF快20倍左右。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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