A clustering density-based sample reduction method

Abstract

In this paper, we propose a new cluster-based sample reduction method which is unsupervised, geometric, and density-based. The original data is initially divided into clusters, and each cluster is divided into "portions" defined as the areas between two concentric circles. Then, using the proposed geometric-based formulas, the membership value of each sample belonging to a specific portion is calculated. Samples are then selected from the original data according to the corresponding calculated membership value. We conduct various experiments on the NSL-KDD and KDDCup99 datasets. © 2014 Springer International Publishing.