Affiliations: [a] State Key Laboratory for Strength and Vibration,
School of Aerospace Xi'an Jiaotong University, Xi'an, Shaanxi, China | [b] College of Computer Science and Technology, HuaQiao
University, Xiamen, Fujian, China
Correspondence:
[*]
Corresponding author: Cheng Wang, State Key Laboratory for
Strength and Vibration, School of Aerospace Xi'an Jiaotong University, Xi'an,
No.28, Xianning West Road, Shaanxi 710049, China. Tel.: +86 029 82660978;
E-mail: wc071@163.com
Abstract: In order to overcome the problem of classical K-means clustering
algorithm, such as sensitiveness to its initial cluster centers selection and
easiness of falling into local optimization, this paper proposed max-min
K-means clustering algorithm. The algorithm adopts max-min principle to
determine initial cluster centers of K-means clustering algorithm. Comparison
of clustering effects of the new method with that of muilt-restart K-means
clustering algorithm on artificial data set showed the better effect and the
greater excellence of max-min principle. Then, the max-min K-means clustering
algorithm was applied to analyze vibration response signal of cylindrical shell
automatic in order to discover space distribution feature of dynamics response.
Clustering results tallied with feature of dynamic response of cylindrical
shell, which validated the effectiveness and reasonableness of the new
clustering method itself.
Keywords: Signal feature extraction, clustering, K-means, max-min
