A clustering-based adaptive Neighborhood Retrieval Visualizer.

We introduce a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). We maintain the advantages of the conventional NeRV method, while proposing an improvement of the data samples' neighborhood width calculation, in the input and output data space. In the standard NeRV, the data samples' neighborhood widths are determined in an arbitrary manner, in this way, inhibiting the possible quality of the resulting data visualization. We propose to compute the widths adaptively, on the basis of the input data scattering. Therefore, we first perform the preliminary input data clustering, next, we calculate the values of the inner-cluster variances, which convey the information on the input data scattering, then, we assign them to each data sample, and finally, we use them as the basis for the data samples' neighborhood widths determination. The results of the experiments conducted on the three different real datasets confirm the effectiveness and usefulness of the proposed approach.