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ITI əməkdaslarının elmi isləri Elektron kitabxana Konfranslar İnformasiya Sistemi Qəzetlər UOT 004
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 Biblioqrafik təsvir
 Alguliyev , R.M. Weighted clustering for anomaly detection in Big Data / R.M. Alguliyev , R.M. Aliguliyev , Y.N. Imamverdiyev , L.V. Sukhostat // Statistics, Optimization and Information Computing. - 2018. - N: vol.6, no.2.- P. 178-188.
 Annotasiya
 In this paper, a new method for anomaly detection based on weighted clustering is proposed. The weights that were obtained by summing the weights of each point from the data set are assigned to clusters. The comparison is made using seven datasets (of large dimensions) with the k-means algorithm. The proposed approach increases the reliability of data partitioning into groups. Experimental results show that the proposed approach becomes more efficient with increasing size of the analysed dataset.
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