In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-const...
Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M...
In this work we introduce a method for classification and visualization. In contrast to simultaneous methods like e.g. Kohonen SOM this new approach, called KMC/EDAM, runs through...
As high performance clusters continue to grow in size, the mean time between failure shrinks. Thus, the issues of fault tolerance and reliability are becoming one of the challengi...
Mobile devices are still memory-constrained when compared to desktop and laptop computers. Thus, in some circumstances, even while occupied by useful objects, some memory must be ...
A number of algorithms of clustering spatial data for reducing the number of disk seeks required to process spatial queries have been developed. One of the algorithms is the scheme...