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Visual analytics for spatial clustering: Using a heuristic approach for guided exploration (2013)
| Content Provider | CiteSeerX |
|---|---|
| Author | Packer, Eli Bak, Peter Polishchuk, Valentin Ship, Harold J. |
| Abstract | Fig. 1. Hierarchical clustering results on a synthetic point dataset (the black dots) are shown as a heatmap. Colored levels of the hierarchy (with darker red colors corresponding to higher levels) highlight those cluster constellations that were found interesting by our heuristic, aiding the user in selecting appropriate algorithmic settings. Abstract—We propose a novel approach of distance-based spatial clustering and contribute a heuristic computation of input parame-ters for guiding users in the search of interesting cluster constellations. We thereby combine computational geometry with interactive visualization into one coherent framework. Our approach entails displaying the results of the heuristics to users, as shown in Figure 1, providing a setting from which to start the exploration and data analysis. Addition interaction capabilities are available containing visual feedback for exploring further clustering options and is able to cope with noise in the data. We evaluate, and show the benefits of our approach on a sophisticated artificial dataset and demonstrate its usefulness on real-world data. Index Terms—Heuristic-based spatial clustering, interactive visual clustering, k-order α-(alpha)-shapes 1 |
| File Format | |
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| Language | English |
| Publisher Date | 2013-01-01 |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
