计算机与数字工程2024，Vol.52Issue(2)：301-306,6.DOI:10.3969/j.issn.1672-9722.2024.02.001
基于混合近邻局部分布差异的离群点检测算法
Outlier Detection Based on Local Distribution Difference of Hybrid Nearest Neighbors
摘要
Abstract
Outlier detection is an important task in the field of data mining.Its purpose is to find inconsistent data from the da-ta representing events or object behaviors.At present,most traditional unsupervised outlier detection algorithms,such as methods based on distance or density,have the problem of declining detection accuracy due to the curse of dimensionality when identifying outlier data in multi-dimensional space.This paper proposes an outlier detection algorithm based on hybrid nearest neighbors.The algorithm uses the hybrid nearest neighbors of data items as a new local influence space,and redefines the similarity calculation method of data items by bidirectional sharing nearest neighbors and Euclidean distance.The average local distribution difference of the sample in its local influence space measures the local outlier degree of the data,so as to identify outliers.The experimental re-sults of comparison with other similar algorithms on synthetic and real data sets prove that this algorithm has a certain improvement in outlier detection.关键词
无监督/离群点检测/混合近邻/局部分布差异Key words
unsupervised/outlier detection/hybrid nearest neighbors/local distribution difference分类
信息技术与安全科学引用本文复制引用
张君,范铭,金举..基于混合近邻局部分布差异的离群点检测算法[J].计算机与数字工程,2024,52(2):301-306,6.基金项目
国家自然科学基金项目(编号:61902306) （编号:61902306）
中国博士后项目(编号:2019TQ0251,2020M673439)资助. （编号:2019TQ0251,2020M673439）
