Machine Learning Approach for Sequence Clustering with Applications to Ground-Motion Selection
Publication: Journal of Engineering Mechanics
Volume 146, Issue 6
Abstract
Clustering analysis of sequential data is of great interest and importance in many science and engineering areas thanks to the explosive growth of time-series data. Effective methods, especially for sequence clustering, are strongly needed to extract features from data for better representation learning. This paper presents an unsupervised machine learning algorithm for sequence clustering based on dynamic -means. Specifically, the clustering problem is firstly formulated rigorously to an optimization problem, which is then solved by a proposed three-step alternating-direction optimization approach. The performance of the proposed approach is successfully illustrated through three examples with both synthetic data sets and field ground-motion measurements. In particular, this approach is applied to ground-motion clustering/selection and shows satisfactory results. Overall, the results demonstrate that the proposed algorithm is able to effectively cluster sequential data through mining latent inherent characteristics.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies (https://github.com/zhry10/Clustering).
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Published In
Journal of Engineering Mechanics
Volume 146 • Issue 6 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jun 12, 2019
Accepted: Dec 3, 2019
Published online: Mar 18, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 18, 2020
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