Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kumar, R.P., Nagabhushan, P. (2006). WaveSim Transform for Multi-channel Signal Data Mining Through Linear Regression PCA. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_106
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DOI: https://doi.org/10.1007/11811305_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
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