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    ARTICLE

    Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

    Haiwen Chen1, Guang Yu1, Fang Liu2, Zhiping Cai1, *, Anfeng Liu3, Shuhui Chen1, Hongbin Huang1, Chak Fong Cheang4

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 917-927, 2020, DOI:10.32604/cmc.2020.05981

    Abstract For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled More >

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