Selection of best clustering features in a wavelet packet library

We develop a new algorithm to select the basis that provides an optimal clustering of nonstationary multidimension data. This optimality is obtained according to a new cost function that measures the clustering power of a basis vector or feature. Our approach does not require any training sets or an...

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Bibliographic Details
Main Authors: Chinrungrueng, J., Meyer, F.G.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Information, signal and communications theory
Libraries
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Wavelet packets
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Description
Summary:We develop a new algorithm to select the basis that provides an optimal clustering of nonstationary multidimension data. This optimality is obtained according to a new cost function that measures the clustering power of a basis vector or feature. Our approach does not require any training sets or any models of the clusters. The basis is searched in a dictionary of wavelet packets. We have conducted experiments with synthetic data. Our method is capable of discovering the structures of the datasets.
DOI:10.1109/TENCON.2004.1414401