Combining topological clustering and multidimensional scaling for visualizing large data sets
Dominique Brodbeck, Luc Girardin · 1998
We present a new strategy for dimensionality reduction which address the performance issues created by large data sets. It relies on the self-organizing map algorithm to perform topological pre-clustering, and on a spring model to achieve metric multidimensional scaling. By taking advantage of the distinct properties of these two methods, we provide a framework which can flexibly balance between qualityand speed-on-demand. Our approach permits the exploration of large data sets and delivers an intuitive frame of reference to the user.