Reducing the James–Stein Shrinkage Estimator for Automatically Grouping Heterogeneous Production Batches

A reduction in the James–Stein shrinkage estimator might significantly increase the accuracy of cluster analysis of k -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 batc...

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Bibliographic Details
Published in:Journal of machinery manufacture and reliability 2024-06, Vol.53 (3), p.254-262
Main Authors: Akhmatshin, F. G., Petrova, I. A., Kazakovtsev, L. A., Kravchenko, I. N.
Format: Article
Language:English
Subjects:
Algorithms
Automation and Control in Engineering
Cluster analysis
Clustering
Engineering
Integrated circuits
Machines
Manufacturing
Parameters
Processes
Shrinkage
Standard data
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Summary:A reduction in the James–Stein shrinkage estimator might significantly increase the accuracy of cluster analysis of k -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 k- 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.
ISSN:1052-6188
1934-9394
DOI:10.1134/S1052618824700043