Abstract
The thematic composition of document collections is commonly conceptualized by clusters of high-dimensional point clouds. However, illustrating these clusters is challenging: typical visualizations such as colored projections or parallel coordinate plots suffer from feature occlusion and noise covering the whole visualization. We propose a method that avoids structural occlusion by using topology-based visualizations to preserve primary clustering features and neglect geometric properties that cannot be preserved in low-dimensional representations. Abstracting the input points as nested dense regions with individual properties, we provide the user with intuitive landscape visualizations that illustrate the high-dimensional clustering structure occlusion-free.
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Oesterling, P., Heine, C., Weber, G.H., Scheuermann, G. (2014). A Topology-Based Approach to Visualize the Thematic Composition of Document Collections. In: Biemann, C., Mehler, A. (eds) Text Mining. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-12655-5_4
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DOI: https://doi.org/10.1007/978-3-319-12655-5_4
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