A prediction-based visual approach for cluster exploration and cluster validation by HOV

Abstract

Predictive knowledge discovery is an important knowledge acquisition method. It is also used in the clustering process of data mining. Visualization is very helpful for high dimensional data analysis, but not precise and this limits its usability in quantitative cluster analysis. In this paper, we adopt a visual technique called HOV3 to explore and verify clustering results with quantified measurements. With the quantified contrast between grouped data distributions produced by HOV3, users can detect clusters and verify their validity efficiently. © Springer-Verlag Berlin Heidelberg 2007.