Research Article
Anomaly Detection of Big Data Based on Improved Fast Density Peak Clustering Algorithm
@INPROCEEDINGS{10.1007/978-3-031-50577-5_24, author={Fulong Zhong and Tongxi Lin}, title={Anomaly Detection of Big Data Based on Improved Fast Density Peak Clustering Algorithm}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III}, proceedings_a={ICMTEL PART 3}, year={2024}, month={2}, keywords={Improved Density Peak Fast Peak Peak Clustering Algorithm Big Data Exception Abnormal Detection}, doi={10.1007/978-3-031-50577-5_24} }- Fulong Zhong
Tongxi Lin
Year: 2024
Anomaly Detection of Big Data Based on Improved Fast Density Peak Clustering Algorithm
ICMTEL PART 3
Springer
DOI: 10.1007/978-3-031-50577-5_24
Abstract
Aiming at the problems of traditional clustering algorithms in the process of large amount of big data anomaly monitoring under the background of big data, a big data anomaly detection method based on improved fast density peak clustering algorithm is proposed. Set up a big data anomaly clustering framework, automatically select parameters and clustering centers, evaluate big data outliers through standardized local density and distance, and can obtain outliers, extract and calculate thresholds according to features, complete anomaly scheduling, and achieve anomaly detection. The clustering algorithm designed in this paper is used for example analysis, which shows that the algorithm designed in this paper can meet the needs of actual users and improve the detection accuracy of power big data outliers.
