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Algorithm for Anomaly Detection in Big Data proposed
20 Mai 2019 - 13:59 | Important events
An article PSO+K-means Algorithm for Anomaly Detection in Big Data, DOI: 10.19139/soic.v7i2.623 was published in “Statistics, Optimization & Information Computing” journal.
The authors of the article are vice-president of ANAS, the director of the Institute of Information Technology, academician Rasim Alguliyev, the head of department of the institute, correspondent member of ANAS Ramiz Aliguliyev and the leading researcher PhD Fargana 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 the 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.
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