Abstract
In many applications, domain-specific entities are easily compared and categorized if they are represented as high-dimensional feature vectors. To detect object similarities and to quantify coherent groups, analysts often visualize the vectors directly, aiming to identify clusters visually. However, common visualizations for high-dimensional data often suffer from information loss, occlusions and visual clutter for large and noisy data. In this case, structure is misleading and false insights are derived. We use topological concepts to provide a structural view of the points. We analyze them in their original space and depict their clustering structure using intuitive landscapes. We describe the visual analysis process to define and simplify the structural view and to perform local analysis by linking individual features to other visualizations.
About the authors
Patrick Oesterling received the MS degree (Diplom) in computer science in 2009 from the University of Leipzig, Germany. He is currently a PhD candidate at the Department of Computer Science at the University of Leipzig, where his research focuses on computer graphics, information visualization, and visual analytics.
Universität Leipzig, Institute of Computer Science, D-04109 Leipzig
Patrick Jähnichen received his B.S. from the University of Cooperative Education in Stuttgart, Germany and his M.S. from the University of Leipzig, Germany, both in Computer Science. He is currently pursuing his PhD in the NLP group at the department of Computer Science at the University of Leipzig. His main interests are knowledge extraction from large document collections, especially topic modeling and scalable machine learning algorithms.
Universität Leipzig, Institute of Computer Science, D-04109 Leipzig
Gerhard Heyer has studied at Cambridge University and the University of the Ruhr, where he received his Ph.D. After research on AI based natural language processing at the University of Michigan he has worked in industry for several years. He holds the chair on Natural Language Processing at the computer science department of the University of Leipzig. His field of research is focused on automatic semantic processing of natural language text with applications in the area of information retrieval and search as well as knowledge management. He is a member of the IEEE Computer Society.
Universität Leipzig, Institute of Computer Science, D-04109 Leipzig
Gerik Scheuermann received the master's degree (diplom) in mathematics in 1995 and the PhD degree in computer science in 1999, both from the Technical University of Kaiserslautern. He is a full professor at the University of Leipzig since 2004. He is a co-author of more than 120 reviewed book chapters, journal, or conference papers. His current research interests focus on visualization with a focus on topology-based methods, flow visualization, visualization for life sciences, and visualization of text collections. He has served as paper co-chair for Eurovis 2008, IEEE Visualization 2011, IEEE Visualization 2012 and as General Chair of Eurovis 2013.
Universität Leipzig, Institute of Computer Science, D-04109 Leipzig
©2015 Walter de Gruyter Berlin/Boston
