关键词: 离群点检测, 聚类, 静态数据集, 数据流, 大规模数据集

Abstract:

Outlier detection has great significance in data processing. At present, its detection methods can be roughly divided into statistical based, distance based, density based and clustering based. In order to grasp the current research status of outlier detection methods based on clustering technology, this paper introduces and classifies the representative outlier detection methods based on clustering, which are mainly divided into four categories：detection methods in static data sets, detection methods in data streams, detection methods in large-scale data and other methods. The problems, algorithm ideas, application scenarios and their advantages and disadvantages of each method are summarized and analyzed in detail, and the existing problems are analyzed and the future development direction is proposed.

Key words: outlier detection, clustering, static data, data stream, large-scale data set