Outlier detection,as an important research direction in the field of data mining,aims to discover data points in a dataset that are different from the majority and have potential analytical value,assistresearchers in identifying potential issues in the data source.Currently,outlier detection has been widely applied in various domains such as fraud detection,smart healthcare,intrusion detection,and fault diagnosis.This study,based on summarizing previous experiences,first discusses the definition of outliers,their causes,and typical application domains.It reviews the advantages and limitations of classical outlier detection algorithms such as DBSCAN and LOF,as well as their improved algorithms.Additionally,it analyzes the advantages of deep learning me-thods in the field of outlier detection.Secondly,considering the requirements for processing massive,high-dimensional,and tempo-ral data in the current internet context,further research is conducted on the development status of outlier detection algorithms in new environments.Finally,the evaluation indicators of outlier detection algorithms,the role of cost factors in outlier detection evaluation,as well as commonly used toolkits and datasets,are introduced.The challenges and future development directions of outlier detection are summarized and prospected.
万方数据
维普
