— Recently several algorithms for clustering large data sets or streaming data sets have been proposed. Most of them address the crisp case of clustering, which cannot be easily ...
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Existing temporal pattern mining assumes that events do not have any duration. However, events in many real world applications have durations, and the relationships among these ev...
Global clustering has rarely been investigated in the area of spatial database systems although dramatic performance improvements can be achieved by using suitable techniques. In ...
We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs. In particular, to gene...
Andy Tsai, Carl-Fredrik Westin, Alfred O. Hero, Al...