HD-Eye: visual clustering of high dimensional data Alexander Hinneburg, Daniel A. Keim, and Markus Wawryniuk   View Paper (PDF)   Return to Demonstrations Abstract Clustering of large data bases is an important research area with a large variety of applications in the data base context. Missing in most of the research efforts are means for guiding the clustering process and understanding the results, which is especially important for high dimensional data. Visualization technology may help to solve this problem since it provides effective support of different clustering paradigms and allows a visual inspection of the results. The HD-Eye (high-dim. eye) system shows that a tight integration of advanced clustering algorithms and state-of-the-art visualization techniques is powerful for a better understanding and effective guidance of the clustering process, and therefore can help to significantly improve the clustering results. DiSC'03 © 2003 Association for Computing Machinery