PSO+K-means Algorithm for Anomaly Detection in Big Data

Rasim M. Alguliyev, Ramiz M. Aliguliyev, Fargana J. Abdullayeva


The use of clustering methods in anomaly detection is considered as an effective approach. The choice of the cluster primary center and the finding of local optimum in the well-known k-means and other classic clustering algorithms are considered as one of the major problems and do not allow to get accurate results in anomaly detection. In this paper to improve the accuracy of anomaly detection based on the combination of PSO (particle swarm optimization) and k-means algorithms, the new weighted clustering method is proposed. The proposed method is tested on Yahoo! S5 dataset and a comparative analysis of the obtained results with the k-means algorithm is performed. The results of experiments show that compared to the k-means algorithm the proposed method is more robust and allows to get more accurate results.


Anomaly detection; Big Data; Particle Swarm Optimization; Clustering; k-means


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DOI: 10.19139/soic.v7i2.623


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