Skip to main content
SpringerLink
Log in

Numerical Time-Series Pattern Extraction Based on Irregular Piecewise Aggregate Approximation and Gradient Specification

  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

This paper proposes and evaluates a method for extracting interesting patterns from numerical time-series data which takes account of user subjectivity. The proposed method conducts irregular sampling on the data preserving the subjectively noteworthy features using a user specified gradient. It also conducts irregular quantization, preserving the intrinsically objective characteristics of the data using statistical distributions. It then extracts representative patterns from the discretized data using group average clustering. Experimental results using benchmark datasets indicate that the proposed method does not destroy the intrinsically objective features, since it has the same performance as the basic subsequence clustering using K-Means algorithm. Results using a dataset from a clinical hepatitis study indicate that it extracts interesting patterns for a medical expert.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Das, G., King-Ip, L., Heikki, M., Renganathan, G. and Smyth, P., “Rule Discovery from Time Series,” in Proc. of Int. Conf. on Knowledge Discovery and Data Mining, pp. 16–22, 1998.

  2. Berndt, D. J. and Clifford, J., “Using dynamic time warping to find patterns in time series,” in Proc. of AAAI Workshop on Knowledge Discovery in Databases, pp. 359–370, 1994.

  3. Hirano, S. and Tsumoto, S., “Mining Similar Temporal Patterns in Long Time-series,” Data and Its Application to Medicine, pp. 219–226, 2002.

  4. Yi, B-K. and Faloutsos, C., “Fast Time Sequence Indexing for Arbitrary Lp Norms,” in Proc. of Int. Conf. on Very Large Databases, pp.385–394, 2000.

  5. Lin, J., Keogh, E., Lonardi, S. and Patel, P., “Finding Motifs in Time Series,” in Proc. of Workshop on Temporal Data Mining, pp. 53–68, 2002.

  6. Tanaka, Y. and Uehara, K., “Discover Motifs in Multi Dimensional Time-Series Using the Principal Component Analysis and the MDL Principle,” in Proc. of Int. Conf. on Machine Learning and Data Mining in Pattern Recognition, pp. 252–265, 2003.

  7. Motoda, H., Active Mining, IOS Press, Amsterdam, 2002.

    MATH  Google Scholar 

  8. Tsumoto, S., Yamaguchi, T., Numao, M. and Motoda, H., “Active Mining Project: Overview,” Lecture Notes in Artificial Intelligence, 3403, pp. 1–10, 2005.

    Google Scholar 

  9. Keogh, E. and Lin, J., “Clustering of Time-Series Subsequences is Meaningless: Implications for Previous and Future Research,” Knowledge and Information Systems, 8 (2), pp.154–177, 2005.

    Article  Google Scholar 

  10. Keogh, E., “Time Series Data Mining Archive,” http://www.cs.ucr.edu/~eamonn/TSDMA/, 2005.

  11. Yang, Y., Carbonell, J., Brwon, R. and Pierce, T., “Learning Approaches for Detecting and Tracking News Events,” IEEE Intelligent Systems Special Issue on Applications of Intelligent Information Retrieval, 14, pp.32–43, 1999.

    Google Scholar 

  12. Ohsaki, M., Sato, Y., Yokoi, H. and Yamaguchi, T., “A Rule Discovery Support System for Sequential Medical Data, – In the Case Study of a Chronic Hepatitis Dataset –,” in Proc. of Int. Workshop on Active Mining, pp. 97–102, 2002.

  13. Tsumoto, S., “ECML/PKDD2002 Discovery Challenge,” http://lisp.vse.cz/challenge/ecmlpkdd2002/, 2002.

Download references

Author information

Authors and Affiliations

  1. Doshisha University, 1-3 Tataramiyakodani, Kyotanabe-shi, Kyoto, 610-0321, Japan

    Miho Ohsaki

  2. Shimane University, 89-1 Enya-cho, Izumo-shi, Shimane, 693-8501, Japan

    Hidenao Abe

  3. Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa, 223-8522, Japan

    Takahira Yamaguchi

Authors
  1. Miho Ohsaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hidenao Abe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takahira Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miho Ohsaki.

About this article

Cite this article

Ohsaki, M., Abe, H. & Yamaguchi, T. Numerical Time-Series Pattern Extraction Based on Irregular Piecewise Aggregate Approximation and Gradient Specification. New Gener. Comput. 25, 213–222 (2007). https://doi.org/10.1007/s00354-007-0013-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00354-007-0013-9

Keywords

Search

Navigation