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Reducing theJames–Stein ShrinkageEstimator forAutomaticallyGroupingHeterogeneousProduction Batches Akhmatshin F.G.Petrova I.A.Kazakovtsev L.A.2024 год

Reducing the James–Stein Shrinkage Estimator for Automatically Grouping Heterogeneous Production Batches : научное издание
статья из журнала

Дополнительные ссылки: eLibrary
База данных: Каталог библиотеки СФУ
Библиографическое описание: Reducing the James–Stein Shrinkage Estimator for Automatically Grouping Heterogeneous Production Batches : научное издание / F. G. Akhmatshin [et al.]. - Текст : непосредственный // Journal of Machinery Manufacture and Reliability. - 2024. - Т.53, №3. - P. 254-262. - The results of this study were obtained as part of implementing project no. NSH-421.2022.4, provided by the Council for Grants of the President of the Russian Federation for government support of Russia’s leading research schools, and project no. 075-15-2022-1121 of the Ministry of Education and Science of the Russian Federation (Megagrant). - ISSN 1052-6188. - ISSN 1934-9394, DOI 10.1134/S1052618824700043.
Аннотация: A reduction in the James–Stein shrinkage estimator might significantly increase the accuracy of cluster analysis of <i>k</i>-means for a relatively broad range of data. The efficiency of using the James–Stein shrinkage estimator for automatically grouping industrial products in homogeneous production batches is considered. Tests are conducted for batches of integrated circuits by comparing the shrinkage results with those obtained using the traditional <i>k-</i>means algorithm. The dataset is normalized according to the values of the acceptable drift, acceptable parameters, and standard deviation. As established using the Rand index, clustering is far more accurate in the automatic grouping of industrial products in homogeneous production batches, when average values of inconclusive parameters drop to zero. It is established that the reduction of the James–Stein shrinkage estimator decreases the influence of inconclusive parameters of standard data to acceptable values.
Год издания: 2024
Авторы: Akhmatshin F.G. , Petrova I.A. , Kazakovtsev L.A. , Kravchenko I.N.
Издательство: Allerton Press, Inc., Москва
Источник: Journal of Machinery Manufacture and Reliability
Выпуск: Т.53, №3
Номера страниц: 254-262
Держатель оригинала документа: Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN), Reshetnev Siberian State University of Science and Technology, Siberian Federal University
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