Abstract
This paper presents a novel method called modified multiscale matching, that enable us to multiscale structural comparison of irregularly-sampled, different-length time series like medical data. We revised the conventional multiscale matching algorithm so that it produces sequence dissimilarity that can be further used for clustering. The main improvements are: (1) introduction of a new segment representation that elude the problem of shrinkage at high scales, (2) introduction of a new dissimilarity measure that directly reflects the dissimilarity of sequence values. We examined the usefulness of the method on the cylinder-bell-funnel dataset and chronic hepatitis dataset. The results demonstrated that the dissimilarity matrix produced by the proposed method, combined with conventional clustering techniques, lead to the successful clustering for both synthetic and real-world data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Keogh, E.: Mining and Indexing Time Series Data. In: Tutorial at the 2001 IEEE International Conference on Data Mining (2001)
Chu, S., Keogh, E.J., Hart, D., Pazzani, M.J.: Iterative Deepening Dynamic Time Warping for Time Series. In: Proc. the Second SIAM Int’l Conf. Data Mining, pp. 148–156 (2002)
Sankoff, D., Kruskal, J.: Time Warps, String Edits, and Macromolecules. CLSI Publications (1999)
Das, G., Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule Discovery from Time Series. Knowledge Discovery and Data Mining, 16–22 (1998)
Keogh, E., Lin, J., Truppel, W.: Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. In: Proc. the IEEE ICDM 2003, pp. 115–122 (2003)
Chan, K.P., Fu, A.W.: Efficient Time Series Matching by Wavelets. In: Proc. the IEEE ICDM 1999, pp. 126–133 (2003)
Kawagoe, K., Ueda, T.: A Similarity Search Method of Time Series Data with Combination of Fourier and Wavelet Transforms. In: Proc. the IEEE TIME 2002, pp. 86–92 (2002)
Witkin, A.P.: Scale-space filtering. In: Proc. the Eighth IJCAI, pp. 1019–1022 (1983)
Lindeberg, T.: Scale-Space for Discrete Signals. IEEE Trans. PAMI 12(3), 234–254 (1990)
Dudek, G., Tostsos, J.K.: Shape Representation and Recognition from Multiscale Curvature. Comp. Vis. Img Understanding 68(2), 170–189 (1997)
Babaud, J., Witkin, A.P., Baudin, M., Duda, O.: Uniqueness of the Gaussian kernel for scale-space filtering. IEEE Trans. PAMI 8(1), 26–33 (1986)
Mokhtarian, F., Mackworth, A.K.: Scale-based Description and Recognition of planar Curves and Two Dimensional Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(1), 24–43 (1986)
Ueda, N., Suzuki, S.: A Matching Algorithm of Deformed Planar Curves Using Multiscale Convex/Concave Structures. IEICE Transactions on Information and Systems J73-D-II(7), 992–1000 (1990), http://lisp.vse.cz/challenge/ecmlpkdd2002/
Lowe, D.G.: Organization of Smooth Image Curves at Multiple Scales. International Journal of Computer Vision 3, 119–130 (1980)
Saito, N.: Local Feature Extraction and Its Application using a Library of Bases. Ph.D. Thesis, Yale University (1994)
Geurts, P.: Pattern Extraction for Time-Series Classification. In: Proceedings of PAKDD 2001, pp. 115–127 (2001)
Keogh, E., Kasetty, S.: On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Mining And Knowledge Discovery 7, 349–371 (2003)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis. 4th Edn. Arnold Publishers (2001)
Tsumoto, S., Hirano, S., Takabayashi, K.: Development of the Active Mining System in Medicine Based on Rough Sets. Journal of Japan Society of Artificial Intelligence (2005) (in press)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hirano, S., Tsumoto, S. (2005). Clustering Time-Series Medical Databases Based on the Improved Multiscale Matching. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_63
Download citation
DOI: https://doi.org/10.1007/11425274_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25878-0
Online ISBN: 978-3-540-31949-8
eBook Packages: Computer ScienceComputer Science (R0)