Data preparation for data mining
Data preparation for data mining
Fast learning in networks of locally-tuned processing units
Neural Computation
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In this paper, quantization errors of individual variables in k-means quantization algorithm are investigated with respect to scaling factors, variable dependency, and distribution characteristics. It is observed that Z-norm standardation limits average quantization errors per variable to unit range. Two measures, quantization quality and effective number of quantization points are proposed for evaluating the goodness of quantization of individual variables. Both measures are invariant with respect to scaling/variances of variables. By comparing these measures between variables, a sense of the relative importance of variables is gained.