• Register
  • Login

European Journal of Molecular & Clinical Medicine

  • Home
  • Browse
    • Current Issue
    • By Issue
    • By Subject
    • Keyword Index
    • Author Index
    • Indexing Databases XML
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
Advanced Search

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 7, Issue 2
  3. Authors

Online ISSN: 2515-8260

Volume7, Issue2

DTW SIMILARITY MEASURE BASED U-SHAPELETS CLUSTERING ALGORITHM FOR TIME-SERIES DATA

    Arathi M Govardhan A

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3378-3392

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

Time-Series Analysis exhibitedefficient results in delivering significant knowledge in numerous domains.
Most of the investigationon Time-Series Analysis is restrictedwith the
requirementofexpensivecategorized information. This led tothe growth of curiosity in groupingthe timeseries
informationthat does not need any access to categorized information. The clustering time-series
informationcarries out issues that donot prevail in conventional clustering methodologies.,in the
Euclidean space amongst the objects.Therefore,the authorsuggested an innovativeclustertechnique,
forTime-Seriesemploying of DTW similarity measure by extracting unsupervised shapelets. And these
extracted u-shapelets are clustered employing iterative k-means algorithm. The DTW similarity measure
provides better accuracy in formed clusters of proposed methodology compared tothe Metric
EuclidianDistance Measure. The performance of the suggested approach is evaluated employing theRand
Index (RI) Measure. The experimental for this approach was performed on four different Time-Series
data samples and the outcomes showed that the RI measure for the DTW based Time-Series Clustering
Algorithm is more when compared to the Existing ED-basedTime-Series Clustering Algorithm.
Keywords:
    Time-Series Analysis Clustering Unsupervised Shapelets K-means Dynamic Time Warping
  • PDF (678 K)
  • XML
(2020). DTW SIMILARITY MEASURE BASED U-SHAPELETS CLUSTERING ALGORITHM FOR TIME-SERIES DATA. European Journal of Molecular & Clinical Medicine, 7(2), 3378-3392.
Arathi M; Govardhan A. "DTW SIMILARITY MEASURE BASED U-SHAPELETS CLUSTERING ALGORITHM FOR TIME-SERIES DATA". European Journal of Molecular & Clinical Medicine, 7, 2, 2020, 3378-3392.
(2020). 'DTW SIMILARITY MEASURE BASED U-SHAPELETS CLUSTERING ALGORITHM FOR TIME-SERIES DATA', European Journal of Molecular & Clinical Medicine, 7(2), pp. 3378-3392.
DTW SIMILARITY MEASURE BASED U-SHAPELETS CLUSTERING ALGORITHM FOR TIME-SERIES DATA. European Journal of Molecular & Clinical Medicine, 2020; 7(2): 3378-3392.
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 48
  • PDF Download: 123
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
Journal Information

Publisher:

Email:  info@ejmcm.com

  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

Editorial Team:  editor@ejmcm.com

For Special Issue Proposal : chiefeditor.ejmcm@gmail.com / info@ejmcm.com

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus