HDDV: Hierarchical Dynamic Dimensional Visualization for Multidimensional Data

K. Techapichetvanich, A. Datta, and R. Owens (Australia)


multidimensional visualization, information visualization, data mining


Over the past few decades, data have been generated from a variety of sources such as computers and satellites, banking transactions, census data, or from scientific experiments, and the size of data sets has been growing. Visualization techniques are needed to analyse, explore, and enhance insight into these vast amounts of multidimensional data. There is an increasing need to discover correlations, rela tionships, outliers, or hidden phenomena within data sets. This is often difficult if data sets are represented in text form as spreadsheets, data tables, or as simple graphs or charts. In this paper, we present a short overview of visual data mining and focus on a new visualization technique in tegrating data mining, called a Hierarchical Dynamic Di mensional Visualization (HDDV) technique, for analysing and exploring large amounts of multidimensional data. Our technique overcomes screen clutter and the occlusion prob lem. The visualization technique enables visual feedback and direct interaction for analysts to generate queries and examine their hypotheses.

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