DSDC: A Large-scale KPI Clustering Model Based on DWSBD Improved DBSCAN
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v5i2.1973
Abstract
In response to the problem of a large amount of noise, anomalies, and phase shifts in KPI time series, which makes it difficult to obtain the correct number of clusters and good clustering accuracy, this paper proposes an improved DBSCAN based clustering algorithm — DSDC algorithm. Firstly, the underlying shape extraction technique of KPI data is proposed based on the existence of a large amount of noise and anomalies in KPI data. Secondly, the DBSCAN clustering algorithm is used to solve the phase shift problem in KPI time series. Finally, for the problem that the DBSCAN algorithm is parameter sensitive and cannot handle multi-density data, a new similarity measure is proposed, i.e., density-weighted shape-based distance (DWSBD). The experimental results show that the DSDC algorithm has higher ACC, NMI, F-score and shorter clustering time compared with K-Medoids and Spectral clustering algorithms which are also based on the underlying shape extraction technique and DWSBD distance.
Keywords
DBSCAN, DBSCAN, DWSBD, KPI, clustering, density-weighted
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[5]M. F. Hassanin, M. Hassan, and A. Shoeb, “DDBSCAN: Different densities-based spatial clustering of applications with noise”, International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 401–404, 2015.
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[7]R. Hyde, and P. Angelov, “A fully autonomous data density based clustering technique”, IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), pp. 116–123, 2014.
[8]Dau H,Bagnall A,Kamgar K,et al.The UCR time series archive[J].IEEE-CAA Journal of Automatica Sinica,2019,6(6):1293-1305.
[9]Souto M, Coelho A, Faceli K, et al. A Comparison of External Clustering Evaluation Indices in the Context of Imbalanced Data Sets[C]// Neural Networks. IEEE, 2012.
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