Lyudmila V. Sukhostat

Work place: Institute of Information Technology, Azerbaijan National Academy of Sciences, Baku, Azerbaijan



Research Interests: Computational Mathematics, Mathematics of Computing, Mathematics


Lyudmila V. Sukhostat works in the Research Lab at Institute of Information Technology, Azerbaijan National Academy of Sciences. She received the M.Sc. degree in 2011 in Applied Mathematics at Azerbaijan State Oil Academy and Ph.D. degree in 2015 in Computer Science at Institute of Information Technology, Azerbaijan. She has over 20 papers published in international journals and conferences.

Author Articles
An Anomaly Detection Based on Optimization

By Rasim M. Alguliyev Ramiz M. Aliguliyev Yadigar N. Imamverdiyev Lyudmila V. Sukhostat

DOI:, Pub. Date: 8 Dec. 2017

At present, an anomaly detection is one of the important problems in many fields. The rapid growth of data volumes requires the availability of a tool for data processing and analysis of a wide variety of data types. The methods for anomaly detection are designed to detect object’s deviations from normal behavior. However, it is difficult to select one tool for all types of anomalies due to the increasing computational complexity and the nature of the data. In this paper, an improved optimization approach for a previously known number of clusters, where a weight is assigned to each data point, is proposed. The aim of this article is to show that weighting of each data point improves the clustering solution. The experimental results on three datasets show that the proposed algorithm detects anomalies more accurately. It was compared to the k-means algorithm. The quality of the clustering result was estimated using clustering evaluation metrics. This research shows that the proposed method works better than k-means on the Australia (credit card applications) dataset according to the Purity, Mirkin and F-measure metrics, and on the heart diseases dataset according to F-measure and variation of information metric.

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