Affiliations: [a] Department of Computer Science, South China Institute of Software Engineering, Guangzhou University, Guangzhou, China
| [b] Research Institute of Computer Systems, South China University of Technology, Guangzhou, Guangdong, China
| [c] School of Information Engineering, Guangzhou Nanyang Polytechnic College, Guangzhou, China
Correspondence:
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Corresponding author. Mengliang Shao, Department of Computer Science, South China Institute of Software Engineering, Guangzhou University, E-mail: [email protected].
Abstract: Outlier detection is an important branch of data mining. This paper proposes an advanced fast density peak outlier detection algorithm based on the characteristics of big data. The algorithm is an outlier detection method based on the improved density peak clustering algorithm. This paper improves the original algorithm. From the perspective of outlier detection, although it is a clustering idea, it avoids the clustering process, reduces the time complexity of the cluster-based outlier detection algorithm, and absorbs. The outlier detection based on neighbors is not sensitive to data dimensions and other advantages. In the power industry, outlier detection can be used in areas such as grid fault detection, equipment fault detection, and power abnormality detection. The simulation experiment of outlier detection based on the daily load curve of single and multiple transformers in a certain province shows that the improved algorithm can effectively detect outliers in the data.
Keywords: Outlier detection, big data, KNN algorithm, density clustering
