While most parallel task graphs scheduling research has been done in the context of single homogeneous clusters, heterogeneous platforms have become prevalent and are extremely at...
Mean-Shift (MS) is a powerful non-parametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets...
We use cluster analysis as a unifying principle for problems from low, middle and high level vision. The clustering problem is viewed as graph partitioning, where nodes represent ...
If the promise of computational modeling is to be fully realized in higherlevel cognitive domains such as language processing, principled methods must be developed to construct th...
Performing data mining tasks in streaming data is considered a challenging research direction, due to the continuous data evolution. In this work, we focus on the problem of clust...
Maria Kontaki, Apostolos N. Papadopoulos, Yannis M...