research-article

An efficient implementation of anytime k-medoids clustering for time series under dynamic time warping

Authors:

Duong Tuan AnhAuthors Info & Claims

SoICT '16: Proceedings of the 7th Symposium on Information and Communication Technology

Pages 22 - 29

https://doi.org/10.1145/3011077.3011128

Published: 08 December 2016 Publication History

Abstract

Time series clustering is one of the crucial tasks in time series data mining. So far, time series clustering has been most used with Euclidean distance. Dynamic Time Warping (DTW) distance measure has increasingly been used as a similarity measurement for various data mining tasks in place of traditional Euclidean distance due to its superiority in sequence-alignment flexibility. However, there exist some difficulties in clustering time series with DTW distance, for example, the problem of speeding up DTW distance calculation in the context of clustering. So far, there have been two proposed methods for time series clustering with DTW and both of them work in batch scheme. Recently, Zhu et al. proposed a framework of anytime clustering for time series with DTW which uses a data-adaptive approximation to DTW. In this paper, we present an efficient implementation of anytime K-medoids clustering for time series data with DTW distance. In our method, we exploit the anytime clustering framework with DTW proposed by Zhu et al., apply a method for medoid initialization, and develop a multithreading technique to speed-up DTW distance calculation. Experimental results on benchmark datasets validate our proposed implementation method for anytime K-medoids clustering for time series with DTW.

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Cited By

Kumamoto KEndo Y(2024)Whole Time-Series Clustering by Embedding Relational Graphs into Vector Spaces Using DeepWalk2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)10.1109/SCISISIS61014.2024.10760058(1-6)Online publication date: 9-Nov-2024
https://doi.org/10.1109/SCISISIS61014.2024.10760058
Suris FBakar MAriff NMohd Nadzir MIbrahim K(2022)Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time WarpingAtmosphere10.3390/atmos1304050313:4(503)Online publication date: 22-Mar-2022
https://doi.org/10.3390/atmos13040503
Wu DAn JYu PLin HMa LDuan HDeng N(2021)Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster AnalysisJournal of Medical Internet Research10.2196/2563023:9(e25630)Online publication date: 28-Sep-2021
https://doi.org/10.2196/25630
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Index Terms

An efficient implementation of anytime k-medoids clustering for time series under dynamic time warping
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

SoICT '16: Proceedings of the 7th Symposium on Information and Communication Technology

December 2016

442 pages

ISBN:9781450348157

DOI:10.1145/3011077

General Chairs:
Nguyen Manh Hung
NTT University, Vietnam
,
Huynh Quyet Thang
HUST, Vietnam
,
Program Chairs:
Luc De Raedt
KULeuven, Belgium
,
Yves Deville
UCLouvain, Belgium
,
Marc Bui
EPHE, France
,
Truong Thi Dieu Linh
HUST, Vietnam
,
Publications Chairs:
Dinh Viet Sang
HUST, Vietnam
,
Nguyen Hong Phuong
HUST, Vietnam
,
Nguyen Thi Oanh
HUST, Vietnam

Copyright © 2016 ACM.

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Publication History

Published: 08 December 2016

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Vietnam National University Ho Chi Minh City (VNU-HCM)

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SoICT '16

SoICT '16: Seventh International Symposium on Information and Communication Technology

December 8 - 9, 2016

Ho Chi Minh City, Vietnam

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SoICT '16 Paper Acceptance Rate 58 of 132 submissions, 44%;

Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

Kumamoto KEndo Y(2024)Whole Time-Series Clustering by Embedding Relational Graphs into Vector Spaces Using DeepWalk2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)10.1109/SCISISIS61014.2024.10760058(1-6)Online publication date: 9-Nov-2024
https://doi.org/10.1109/SCISISIS61014.2024.10760058
Suris FBakar MAriff NMohd Nadzir MIbrahim K(2022)Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time WarpingAtmosphere10.3390/atmos1304050313:4(503)Online publication date: 22-Mar-2022
https://doi.org/10.3390/atmos13040503
Wu DAn JYu PLin HMa LDuan HDeng N(2021)Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster AnalysisJournal of Medical Internet Research10.2196/2563023:9(e25630)Online publication date: 28-Sep-2021
https://doi.org/10.2196/25630
Dat DHung P(2021)Improvement for Time Series Clustering with the Deep Learning ApproachCooperative Design, Visualization, and Engineering10.1007/978-3-030-88207-5_8(73-83)Online publication date: 24-Oct-2021
https://dl.acm.org/doi/10.1007/978-3-030-88207-5_8
Gloumakov YSpiers ADollar A(2020)Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Three-, Four-, and Seven-Degree-of-Freedom Arm MovementsIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2020.304052228:12(2826-2836)Online publication date: Dec-2020
https://doi.org/10.1109/TNSRE.2020.3040522
Ji HWang JHan YZhao Q(2019)Fast Fuzzy Clustering Algorithm for Time Series in Industrial Processes2019 IEEE 5th International Conference on Computer and Communications (ICCC)10.1109/ICCC47050.2019.9064295(141-146)Online publication date: Dec-2019
https://doi.org/10.1109/ICCC47050.2019.9064295

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