Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: Springer Science and Business Media Deutschland GmbH; 6 July 2020 through 10 July 2020; 6 July 2020 through 10 July 2020

Год издания: 2020

Идентификатор DOI: 10.1007/978-3-030-58657-7_33

Ключевые слова: clustering, electronic radio components, k-means

Аннотация: We propose an optimization model of automatic grouping (clustering) based on the k-means model with the Mahalanobis distance measure. This model uses training (parameterization) procedure for the Mahalanobis distance measure by calculating the averaged estimation of the covariance matrix for a training sample. In this work, we investigate the application of the k-means algorithm for the problem of automatic grouping of devices, each of which is described by a large number of measured parameters, with various distance measures: Euclidean, Manhattan, Mahalanobis. If we have a sample with the composition known in advance, we use it as a training (parameterizing) sample from which we can calculate the averaged estimation of the covariance matrix of homogeneous production batches using the Mahalanobis distance. We propose a new clustering model based on the k-means algorithm with the Mahalanobis distance with the averaged (weighted average) estimation of the covariance matrix. We used various optimization models based on the k-means model in our computational experiments for the automatic grouping (clustering) of electronic radio components based on data from their non-destructive testing results. As a result, our new model of automatic grouping allows us to reach the highest accuracy by the Rand index. © 2020, Springer Nature Switzerland AG.

Журнал: Communications in Computer and Information Science

Выпуск журнала: Vol. 1275 CCIS

Номера страниц: 421-436

ISSN журнала: 18650929

• Shkaberina G.S. (Reshetnev Siberian State University of Science and Technology, prosp. Krasnoyarskiy Rabochiy 31, Krasnoyarsk, 660031, Russian Federation)
• Orlov V.I. (Reshetnev Siberian State University of Science and Technology, prosp. Krasnoyarskiy Rabochiy 31, Krasnoyarsk, 660031, Russian Federation, Testing and Technical Center – NPO PM, 20, Molodezhnaya Street, Zheleznogorsk, 662970, Russian Federation)
• Tovbis E.M. (Reshetnev Siberian State University of Science and Technology, prosp. Krasnoyarskiy Rabochiy 31, Krasnoyarsk, 660031, Russian Federation)
• Kazakovtsev L.A. (Reshetnev Siberian State University of Science and Technology, prosp. Krasnoyarskiy Rabochiy 31, Krasnoyarsk, 660031, Russian Federation, Siberian Federal University, prosp. Svobodny 79, Krasnoyarsk, 660041, Russian Federation)

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