Abstract: This paper aims to improve the accuracy of anomaly data identification in fault diagnosis of vehicle using an improved K-means clustering algorithm. To address the sensitivity of initial ...
Abstract: The traditional K-means algorithm often leads to unstable clustering quality due to the randomness of the initial clustering center selection and tends to fall into suboptimal solutions when ...
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