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LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs

Abstract: 

Time-series clustering involves grouping homogeneous time series together based on certain similarity measures. The mixture AR model (MxAR) has already been developed for time series clustering, as has an associated EM algorithm. How- ever, this EM clustering algorithm fails to perform satisfactorily in large-scale applications due to its high computational complexity. This paper proposes a new algorithm, k-ARs, which is a limiting version of the existing EM algorithm. It shows remarkably good computational performance when applied to large-scale clustering problems as illustrated on some benchmark simulations motivated by some real applications.

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Paper Details

Authors:
Submitted On:
15 May 2020 - 2:55am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Zuogong Yue
Paper Code:
1772
Document Year:
2020
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Document Files

kARs.pdf

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[1] , "LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5339. Accessed: Sep. 22, 2020.
@article{5339-20,
url = {http://sigport.org/5339},
author = { },
publisher = {IEEE SigPort},
title = {LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs},
year = {2020} }
TY - EJOUR
T1 - LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5339
ER -
. (2020). LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs. IEEE SigPort. http://sigport.org/5339
, 2020. LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs. Available at: http://sigport.org/5339.
. (2020). "LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs." Web.
1. . LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5339