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
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