Отрывок: choose the next centroid from these points so that the probability of the point selection is proportional to the calculated square of the distance to it; 4. this can be done as follows. On the Step 2 with the calculation (dx)² we need to calculate the sum (dx²). After accumulating the sum find the value Rand = random (0.0,1.0)*Sum. The Rnd generator randomly indicate the number in the interval [0; Sum), and...
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dc.contributor.authorTarasov, V.N.-
dc.contributor.authorSamarkin, M.E.-
dc.contributor.authorMezenceva, E.M.-
dc.date.accessioned2017-05-19 12:51:20-
dc.date.available2017-05-19 12:51:20-
dc.date.issued2017-
dc.identifierDspace\SGAU\20170516\63798ru
dc.identifier.citationTarasov V.N. Detection of hidden attacks to telecommunication company on the basis of the data mining technologies / V.N. Tarasov, M.E. Samarkin, E.M. Mezenceva // Сборник трудов III международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2017) - Самара: Новая техника, 2017. - С. 867-872.ru
dc.identifier.urihttp://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Detection-of-hidden-attacks-to-telecommunication-company-on-the-basis-of-the-data-mining-technologies-63798-
dc.description.abstractIn this article we consider an approach of analysis the telecommunications company customers information using data mining technology. The purpose of this work is to develop a software solution for identifying customers who behave abnormally compared to ordinary customers. To solve the problem the method of cluster analysis is used, with k-means, method of support vector machines (SVM) and nonstandard method of initializing k-means++ centroids. With the application of the developed programs, the analysis of the data obtained and the identification of customers who are suspected of hidden attacks on the company are identified. To classify the new data the classifier is developed, which is trained on large data sets and based on SVM.ru
dc.language.isoenru
dc.publisherНовая техникаru
dc.subjectcluster analysisru
dc.subjectk-means methodru
dc.subjectthe method of initializing k-means++ centroidsru
dc.titleDetection of hidden attacks to telecommunication company on the basis of the data mining technologiesru
dc.typeArticleru
dc.textpartchoose the next centroid from these points so that the probability of the point selection is proportional to the calculated square of the distance to it; 4. this can be done as follows. On the Step 2 with the calculation (dx)² we need to calculate the sum (dx²). After accumulating the sum find the value Rand = random (0.0,1.0)*Sum. The Rnd generator randomly indicate the number in the interval [0; Sum), and...-
Располагается в коллекциях: Информационные технологии и нанотехнологии

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