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. 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.
IBM Research - Haifa / Israel, Smarter Decision Solutions Group.
Eli
E
Packer
Peter
P
Bak
Mikko
M
Nikkilä
Valentin
V
Polishchuk
Harold J
HJ
Ship
D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface