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. Categories and Subject Descr...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Elias Pampalk, Werner Goebl, Gerhard Widmer
• Keywords: Cluster Structure, Data Mining, Data Mining Projects, Interactive Data Mining, KDD 2003