An efficient and effective algorithm for density biased sampling
Αλέξανδρος Νανόπουλος, Yannis Manolopoulos, Yannis Theodoridis · 2002
In this paper we describe a new density-biased sampling algorithm. It exploits spatial indexes and the local density information they preserve, to provide improved quality of sampling result and fast access to elements of the dataset. It attains improved sampling quality, with respect to factors like skew, noise or dimensionality. Moreover, it has the advantage of efficiently handling dynamic updates, and it requires low execution times. The performance of the proposed method is examined experimentally. The comparative results illustrate its superiority over existing methods.