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Publication Information
Title A Generalized Shapelet Learning Method for Unsupervised Multivariate Time Series Clustering
Abstract Unsupervised multivariate time series clustering is important in many application areas. Among many unsupervised methods Shapelet learning has shown promise for univariate time series signal processing. Discovering suitable Shapelets from a large number of candidate Shapelets has been widely studied for classification of univariate time series signals. However, there is no generalized Shapelet based unsupervised clustering of multivariate time series data. Consequently, this work proposes a generalized Shapelet learning framework for unsupervised multivariate time series clustering. The proposed method utilizes spectral clustering and Shapelet similarity minimization with least square regularization to obtain the optimal multivariate Shapelets for unsupervised clustering. The proposed method is evaluated using an in-house multivariate time series dataset on detection of radio frequency (RF) faults in the Jefferson Labs Continuous Beam Accelerator Facility (CEBAF). The dataset constitut
Author(s) Christopher Tennant, Adam Carpenter, Lasitha Vidyaratne, Md. Monibor Rahman, K. Iftekharuddin, Alexander Glandon, Anna Shablina
Publication Date July 2021
Category Computational Physics
Document Type Meeting
Primary Institution Old Dominion University, Norfolk, VA
Affiliation Accelerator Ops, R&D / Cntr-Adv Studies of Acce / Ctr for Adv Stud of Accel
Funding Source Nuclear Physics (NP)
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-ACP-21-3323 OSTI Number:
LANL Number: Other Number: DOE/OR/23177-5137
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding Yes
LDRD Number(s)
2020-LDRD-3
Meeting / Conference
Meeting Name International Joint Conference on Neural Networks
Meeting Date 7/18/2021
Document Subtype Paper (proceedings only)
Attachments/Datasets/DOI Link
Document(s)
Dataset(s) (none)
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