Visual Analytics for Spatial Clustering: Using a Heuristic Approach for Guided Exploration
We propose a novel approach of distance-based spatial clustering and contribute a heuristic computation of input parameters for guiding users in the search of interesting cluster constellations. Our approach entails displaying the results of the heuristics to users, providing a setting to start the exploration from. We provide in addition interaction capabilities with visual feedback for exploring further clustering options and the ability to cope with noise in the data. We
evaluate our approach on a sophisticated artificial dataset and demonstrate its usefulness on real-world data. Our evaluations reveal the performance and behavior of our approach under different conditions and prove beneficial for exploring complex clusters in sets of data.
Joint work with Peter Bak, Mikko Nikkila, Valentin Polishchuk, and Harold J. Ship