A new density-based sampling for clustering algorithm

As clustering algorithms become more and more sophisticated to cope with current needs large data sets of increasing complexity sampling is likely to provide an interesting alternative. The main objective of sampling is to select a part that behaves like the whole. DENDIS is a new algorithm that combines the best of the available techniques in such a way that tractability is actually improved with a user friendly parameter setting.