Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
In this paper we present a novel technique for nearest neighbor searching dubbed neighborhood approximation. The central idea is to divide the database into compact regions repres...
One of the most well-studied problems in data mining is computing association rules from large transactional databases. Often, the rule collections extracted from existing datamin...
The Self Organizing Map (SOM) involves neural networks, that learns the features of input data thorough unsupervised, competitive neighborhood learning. In the SOM learning algorit...
We introduce a new approach to the problem of collision detection between a rotating milling-cutter of an NC-machine and a model of a solid workpiece, as the rotating cutter conti...
Ron Wein, Oleg Ilushin, Gershon Elber, Dan Halperi...