Discovering regular groups of mobile objects using incremental clustering
Sigal Elnekave, Mark Last, Oded Maimon, Yehuda Ben‐Shimol, Hans Einsiedler, Menahem Friedman, Matthias Siebert · 2008
As technology advances, detailed data on the position of moving objects, such as humans and vehicles is available. In order to discover groups of mobile objects that usually move in similar ways we propose an incremental clustering algorithm that clusters mobile objects according to similarity of their movement patterns. The proposed clustering algorithm uses a new, "data-amount-based" similarity measure between mobile trajectories. The clustering algorithm is evaluated on two spatio-temporal datasets using clustering validity measures.