document: Visualizing changes in the structure of data for exploratory feature selection Using visualization techniques to explore and understand high-dimensional data is an efficient way to combine human intelligence with the immense brute force computation power available nowadays . Several visualization techniques have been developed to study the cluster structure of data , i.e. , the existence of distinctive groups in the data and how these clusters are related to each other . However , only few of these techniques lend themselves to studying how this structure changes if the features describing the data are changed . Understanding this relationship between the features and the cluster structure means understanding the features themselves and is thus a useful tool in the feature extraction phase . In this paper we present a novel approach to visualizing how modification of the features with respect to weighting or normalization changes the cluster structure . We demonstrate the application of our approach in two music related data mining projects .
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