Clustering of large databases 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 understand the results, which is especially important if the data under consideration is high dimensional and has not been collected for the purpose of being analyzed. Visualization technology may help to solve this problem since it allows an effective support of different clustering paradigms and provides means for a visual inspection of the results. Our HD-Eye (high-dimensional eye) system (A. Hinneburg et al., 1999) 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. The demonstration shows how the user can visually explore the data by focusing on interesting projections and guide the important steps of the clustering process. Due to its interactive nature, the HD-Eye system allows a combination of multiple clustering paradigms, leading to clustering models, which fit, well to the intended tasks and the users interests. In addition, the integrated data visualization capabilities of the HD-Eye system lead to a better understanding of the clustering results. The applications to be demonstrated include clustering of large image as well as molecular biology databases.

We are proposing a novel method that makes it possible to analyze high-dimensional data with arbitrary shaped projected clusters and high noise levels. At the core of our method lies the idea of subspace validity. We map the data in a way that allows us to test the quality of subspaces using statistical tests. Experimental results, both on synthetic and real data sets, demonstrate the potential of our method.

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.