Machine Learning Approach for Sequence Clustering with Applications to Ground-Motion Selection-Reference-Cited by-同舟云学术

Machine Learning Approach for Sequence Clustering with Applications to Ground-Motion Selection

Published:2020-06 Issue:6 Volume:146 Page:04020040
ISSN:0733-9399
Container-title:Journal of Engineering Mechanics
language:en
Short-container-title:J. Eng. Mech.

Author:

Zhang Ruiyang¹^ORCID,Hajjar Jerome²,Sun Hao³^ORCID

Affiliation:

1. Postdoctoral Associate, Dept. of Civil and Environmental Engineering, Northeastern Univ., Boston, MA 02115. ORCID: .

2. Camp Dresser & McKee Smith Professor and Chair, Dept. of Civil and Environmental Engineering, Northeastern Univ., Boston, MA 02115.

3. Assistant Professor, Dept. of Civil and Environmental Engineering, Northeastern Univ., Boston, MA 02115; Research Affiliate, Dept. of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Mechanics of Materials

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29EM.1943-7889.0001766

Reference37 articles.

1. Time-series clustering – A decade review

2. FUZZY PATTERN CLASSIFICATION OF STRONG GROUND MOTION RECORDS

3. Incremental, online, and merge mining of partial periodic patterns in time-series databases

4. Conditional Mean Spectrum: Tool for Ground-Motion Selection

5. A simulation study on clustering time series with metaheuristic methods;Baragona R.;Quaderni di Statistica,2001

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