A.V. Skatkov1, A.A. Bryukhovetskiy1, D.V. Moiseev1, Iu.E. Shishkin1,2
1 Federal State Educational Institution of Higher Education «Sevastopol State University», Russian Federation, Sevastopol, Universitetskaya St., 33
2 Institute of Natural and Technical Systems, Russian Federation, Sevastopol, Lenin St., 28
Data monitoring on environment industrial pollution is associated with filtering, processing and accumulation of multidimensional, heterogeneous, dynamically updated data. The processing of such data by traditional methods is very difficult because they are semistructured. Therefore, it seems promising to use clustering operations, which have proven effective in such cases. Thus, a problem arises concerning the detection of anomalous values in such data which should be solved on the basis of clustering.
Since known methods for identifying anomalous values are oriented for data presented in vector or matrix form, it becomes relevant to develop methods for detecting anomalies in cluster form data. To solve this problem, the Kullback measure is used, which is known as the information characteristic for data represented by series of distributions. In this case, it is proposed to use the Kullback measure as a tool for numerical metric calculation of dynamically changing clusters and their number. To implement the operational component of the Kullback measure, a complete graph is used that characterizes the Kullback information measure as the distance between the classes in question.
An example of the implementation of the proposed approach by numerical modeling and graphical illustration of the dynamic process of cluster formation and their power is given. An algorithm is proposed for dynamically correcting the structure of classes and their number, obtaining current data and the results of their presentation in the form of the described graphs and distance characteristics is proposed. On this basis, an adaptive decision-making procedure in the uncertainty conditions is formed.
Keywords: analysis, anomalies, big data, clustering, Kullback measure, monitoring, environment, forecasting, ecosystems.
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