Tandem Fusion of Nearest Neighbor Editing and Condensing Algorithms - Data Dimensionality Effects

In this paper, the effect of the dimensionality of data sets on the exploitation of synergy among known nearest neighbor (NN) editing and condensing tools is analyzed using a synthetic data set. The synergy is exploited through a tandem mode of fusion approach that combines the proximity graph (PG) based editing scheme and the minimal consistent set (MCS) condensing technique. These two methods were selected on the basis of prior experience to representatively evaluate the effect of the data dimensionality. The algorithm level fusion of PG editing and MCS condensing is experimentally shown to be a powerful implement across the range of data dimensionality.