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HD-Eye - visual clustering of high dimensional data: a demonstration

机译：HD-Eye-高维数据的视觉聚类：演示

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摘要

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.
机译：大型数据库的集群是重要的研究领域，在数据库环境中具有多种应用程序。大多数研究工作中缺少的是用于指导聚类过程和理解结果的方法，如果所考虑的数据是高维且尚未出于分析目的而收集的，则这一点尤其重要。可视化技术可以帮助解决此问题，因为它可以有效支持不同的聚类范例，并提供可视化检查结果的方法。我们的HD-Eye（高维眼）系统（A. Hinneburg等人，1999）表明，先进的聚类算法和最新的可视化技术的紧密集成对更好地理解和有效指导聚类过程，因此可以帮助显着改善聚类结果。该演示展示了用户如何通过关注有趣的投影来直观地浏览数据并指导聚类过程的重要步骤。由于其交互性，HD-Eye系统允许多个聚类范例的组合，从而导致聚类模型非常适合预期的任务和用户的兴趣。此外，HD-Eye系统的集成数据可视化功能可更好地了解聚类结果。要演示的应用程序包括大图像的聚类以及分子生物学数据库。

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