Cluster rendering of skewed datasets via visualization
Keke Chen, Ling Liu · 2003
Information Visualization is commonly recognized as a useful method for understanding sophistication in large datasets. In this paper, we introduce a flexible clustering approach with visualization techniques, aiming at the datasets that have skewed cluster distribution. This paper has three contributions. First, we propose a framework Vista that incorporates information visualization methods into the clustering process in order to enhance the understanding of the intermediate clustering results and allow user to revise the clustering results easily. Second, we develop a visualization model that maps multidimensional dataset to 2D visualizations while preserving or partially preserving clusters. Third, based on the visualization model, a set of operating rules are proposed to guide the user rendering clusters efficiently. Experiments show that the Vista system can yield lower error rates for real datasets than typical automated algorithms.