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

Muhammad Marwan Muhammad Fuad

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.

References

  1. Agrawal, R., Faloutsos, C., & Swami, A. (1993): Efficient similarity search in sequence databases. Proceedings of the 4th Conf. on Foundations of Data Organization and Algorithms.
  2. Agrawal, R., Lin, K. I., Sawhney, H. S. and Shim, K. (1995): Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings of the 21st Int'l Conference on Very Large Databases. Zurich, Switzerland.
  3. Bellman, R., (1957): Dynamic programming. Princeton University Press, Princeton, NJ.
  4. Bunke, H., Kraetzl, M. (2003): Classification and detection of abnormal events in time series of graphs. In: Last, M., Kandel, A., Bunke, H. (eds.): Data mining in time series databases. World Scientific.
  5. Chen,Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., and Batista, G. (2015). The UCR Time Series Classification Archive. URL. www.cs.ucr.edu/eamonn/time_series_data.
  6. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., and Keogh, E. (2008): Querying and mining of time series data: experimental comparison of representations and distance measures. In Proc of the 34th VLDB.
  7. Faloutsos, C., Ranganathan, M., and Manolopoulos, Y. (1994): Fast subsequence matching in time-series databases. In Proc. ACM SIGMOD Conf., Minneapolis.
  8. Gorunescu, F. (2006): Data mining: concepts, models and techniques, Blue Publishing House, Cluj-Napoca.
  9. Guo, A.Y., and Siegelmann, H. (2004): Time-warped longest common subsequence algorithm for music retrieval, in Proc. ISMIR.
  10. Keogh, E., Chakrabarti, K., Pazzani, M. & Mehrotra,S. (2000): Dimensionality reduction for fast similarity search in large time series databases. J. of Know. and Inform. Sys.
  11. Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. (2001): Locally adaptive dimensionality reduction for similarity search in large time series databases. SIGMOD pp 151-162.
  12. Kolesnikov, A., and Franti, P. (2003): Reduced-search dynamic programming for approximation of polygonal curves. Pattern Recognition Letters.
  13. Kanungo, T., Netanyahu, N.S., Wu, A.Y. (2002): An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern analysis and machine intelligence 24(7).
  14. Larose, D.T. (2005): Discovering knowledge in data: an introduction to data mining. New York, Wiley.
  15. Marteau, P.F., and Gibet, S. (2005): Adaptive sampling of motion trajectories for discrete task-based analysis and synthesis of gesture. In Proc. of Int. Gesture Workshop, Vannes, France.
  16. Marteau, P.F., Ménier, G. (2006): Adaptive multiresolution and dedicated elastic matching in linear time complexity for time series data mining, Sixth International conference on Intelligent Systems Design and Applications (ISDA 2006), Jinan Shandong, China, 16-18 October.
  17. Morinaka, Y., Yoshikawa, M., Amagasa, T., and Uemura, S. (2001): The L-index: an indexing structure for efficient subsequence matching in time sequence databases. In Proc. 5th PacificAsia Conf. on Knowledge Discovery and Data Mining, pages 51-60.
  18. Perez, J. C., and Vidal, E. (1994): Optimum polygonal approximation of digitized curves. Pattern Recognition Letters.
  19. Vlachos, M., and Gunopulos, D. (2003): Indexing timeseries under conditions of noise. In: Last, M., Kandel, A., Bunke, H. (eds.): Data mining in time series databases. World Scientific.
  20. Yi, B, K., & Faloutsos, C. (2000): Fast time sequence indexing for arbitrary Lp norms. Proceedings of the 26st International Conference on Very Large Databases, Cairo, Egypt.
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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