An experimental evaluation of the adaptive sampling method for time series classification and clustering

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Adaptive sampling is a dimensionality reduction technique of time series data inspired by the dynamic programming piecewise linear approximation. This dimensionality reduction technique yields a suboptimal solution of the problem of polygonal curve approximation by limiting the search space. In this paper, we conduct extensive experiments to evaluate the performance of adaptive sampling in 1-NN classification and k-means clustering tasks. The experiments we conducted show that adaptive sampling gives satisfactory results in the aforementioned tasks even for relatively high compression ratios.

Original languageEnglish
Title of host publicationICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
EditorsAna Fred, Maria De Marsico, Gabriella Sanniti di Baja
PublisherSciTePress
Pages48-54
Number of pages7
ISBN (Electronic)9789897581731
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016 - Rome, Italy
Duration: 24 Feb 201626 Feb 2016
http://www.icpram.org/?y=2016

Publication series

NameICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

Conference

Conference5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016
Abbreviated titleICPRAM 2016
CountryItaly
CityRome
Period24/02/1626/02/16
Internet address

Keywords

  • Adaptive sampling
  • Classification
  • Clustering
  • Data mining
  • Time series

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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