Distance functions are an important component in many learning applications. However, the correct function is context dependent, therefore it is advantageous to learn a distance f...
In most of the proposed clustering algorithms for wireless ad hoc networks, the cluster-heads form a dominating set in the network topology. A variant of dominating set which is mo...
We present a stochastic clustering algorithm which uses pairwise similarity of elements, based on a new graph theoretical algorithm for the sampling of cuts in graphs. The stochas...
In the incremental versions of Facility Location and k-Median, the demand points arrive one at a time and the algorithm must maintain a good solution by either adding each new dema...
The scope of the well-known k-means algorithm has been
broadly extended with some recent results: first, the k-
means++ initialization method gives some approximation
guarantees...