WaveSim Transform for Multi-channel Signal Data Mining Through Linear Regression PCA

  • R. Pradeep Kumar
  • P. Nagabhushan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Temporal data mining is concerned with the analysis of temporal data and finding temporal patterns, regularities, trends, clusters in sets of temporal data. In this paper we extract regression features from the coefficients obtained by applying WaveSim Transform on Multi-Channel signals. WaveSim Transform is a reverse approach for generating Wavelet Transform like coefficients by using a conventional similarity measure between the function f(t) and the wavelet. WaveSim transform provides a means to analyze a temporal data at multiple resolutions. We propose a method for computing principal components when the feature is of linear regression type i.e. a line. The resultant principal component features are also lines. So through PCA we achieve dimensionality reduction and thus we show that from the first few principal component regression lines we can achieve a good classification of the objects or samples. The techniques have been tested on an EEG dataset recorded through 64 channels and the results are very encouraging.


Dimensionality Reduction Dynamic Time Warping Haar Wavelet Temporal Database Multiple Resolution 
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  1. 1.
    Wang, X., Bettini, C., Brodsky, A., Jajodia, S.: Logical design for temporal databases with multiple granularities. ACM Transactions of Database Systems 22(2), 115–170 (1997)CrossRefGoogle Scholar
  2. 2.
    Roddick, J., Hornsby, K.: Temporal, Spatial, and Spatio-Temporal Data Mining. In: First Int’l workshop on Temporal, Spatial, and Spatio-Temporal Data Mining (2000)Google Scholar
  3. 3.
    Pradeep Kumar, R., Nagabhushan, P.: WaveSim Transform – A New Perspective of Wavelet Transform for Temporal Data Clustering. In: IEEE International Conference on Granular Computing (2006)Google Scholar
  4. 4.
    Oates, T., Firoiu, L., Cohen, P.R.: Clustering Time Series with Hidden Markov Models and Dynamic Time Warping. In: International workshop on Times Series Analysis (1999)Google Scholar
  5. 5.
    Gowda, K.C., Diday, E.: Symbolic clustering using a new similarity measure. IEEE Trans. Syst. Man and Cybernet. 22(2), 368–378 (1992)CrossRefGoogle Scholar
  6. 6.
    Diday, E.: An Introduction to Symbolic Data Analysis and Sodas Software. The Electronic Journal of Symbolic Data Analysis 0.0 (2002)Google Scholar
  7. 7.
    Nagabhushan, P., Pradeep Kumar, R.: Curse of Symbolic Dimensions-Overcoming through Histogram PCA and Regression Line PCA. Communicated to Journal of Symbolic Data AnalysisGoogle Scholar
  8. 8.
    Nagabhushan, Gowda, Diday: Dimensionality reduction of symbolic data. Pattern Recognition Letters 16, 219–223 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • R. Pradeep Kumar
    • 1
  • P. Nagabhushan
    • 1
  1. 1.Department of Studies in Computer ScienceUniversity of MysoreIndia

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