The article “Weighted Clustering for Anomaly Detection in Big Data” (DOI: 10.19139 / soic.v6i2.404) co-authered by academician-secretary of ANAS, head of Institute of Information Technology of ANAS, academician Rasim Alguliyev, corresponding member of ANAS, doctor of technical sciences Ramiz Aliguliyev, head of department, doctor of technical sciences, associate professor Yadigar Imamverdiyev and senior researcher Lyudmila Sukhostat published in the  “Statistics, Optimization & Information Computing”  journal published by the US-based “International Academic Press”.

Big data notion is intended to work with information with large scale and different content that is frequently updated. Selecting the appropriate mechanism for detecting anomalies in the Big data is an actual issue. The article suggests an algorithm based on weighted clustering to detect anomalies in real Big data. The comparative analysis of the proposed method with the k-means method was carried out on a large database. The results of experiments have shown that the proposed approach is effective both in clusterization and in the detection of anomalies.

The article was prepared on the basis of a grant project called "Development of methods and algorithms for ensuring information security in Big Data and its applications” funded by the Science Development Fund under the President of the Republic of Azerbaijan (Grant # EIF-KETPL-2-2015-1 (25) - 56/05/1).

“Statistics, Optimization & Information Computing” is indexed and summarized in “Scopus” (Elsevier), “Crossref”, “Google Scholar’, “DOAJ” and other international scientific databases.

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