An algorithm for drawing general undirected graphs
Information Processing Letters
Graph drawing by force-directed placement
Software—Practice & Experience
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
H-BLOB: a hierarchical visual clustering method using implicit surfaces
Proceedings of the conference on Visualization '00
A Fast Multi-scale Method for Drawing Large Graphs
GD '00 Proceedings of the 8th International Symposium on Graph Drawing
Visualizing Informationon a Sphere
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Information Visualization: Perception for Design
Information Visualization: Perception for Design
Dynamic Drawing of Clustered Graphs
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Interactive Visualization of Small World Graphs
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Visual Exploration of Complex Time-Varying Graphs
IEEE Transactions on Visualization and Computer Graphics
An experimental comparison of fast algorithms for drawing general large graphs
GD'05 Proceedings of the 13th international conference on Graph Drawing
GD'04 Proceedings of the 12th international conference on Graph Drawing
Drawing large graphs with a potential-field-based multilevel algorithm
GD'04 Proceedings of the 12th international conference on Graph Drawing
Towards closing the analysis gap: visual generation of decision supporting schemes from raw data
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
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The huge amount of different automatic clustering methods emphasizes one thing: there is no optimal clustering method for all possible cases. In certain application domains, like genomics and natural language processing, it is not even clear if any of the already known clustering methods suffice. In such cases, an automatic clustering method is often followed by manual refinement. The refined version may then be used as either an illustration, a reference, or even an input for a rule based or other machine learning algorithm as a new clustering method. In this paper, we describe a novel interaction technique to manual cluster refinement using the metaphor of soap bubbles, represented by special implicit surfaces (blobs). For instance, entities can simply be moved inside and outside of these blobs. A modified force-directed layout process automatically arranges entities equidistant on the screen. The modifications include a reduction to the expected amount of computation per iteration down to O(|V| log |V|+|E|) in order to achieve a high response time for use in an interactive system. We also spend a considerable amount of effort making the display of blobs fast enough for an interactive system.