An Experimental Evaluation of the Adaptive Sampling Method for Time Series Classification and Clustering

Muhammad Marwan Muhammad Fuad

2016

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.

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Paper Citation


in Harvard Style

Fuad M. (2016). An Experimental Evaluation of the Adaptive Sampling Method for Time Series Classification and Clustering . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 48-54. DOI: 10.5220/0005694600480054


in Bibtex Style

@conference{icpram16,
author={Muhammad Marwan Muhammad Fuad},
title={An Experimental Evaluation of the Adaptive Sampling Method for Time Series Classification and Clustering},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={48-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005694600480054},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - An Experimental Evaluation of the Adaptive Sampling Method for Time Series Classification and Clustering
SN - 978-989-758-173-1
AU - Fuad M.
PY - 2016
SP - 48
EP - 54
DO - 10.5220/0005694600480054