Visualization of an approach to data clustering
Marisabel Guevara · Journal of the Arkansas Academy of Science · 2008
Using visualization and clustering goals as guidelines, this thesis explores a graphic implementation of a data clustering technique that repositions vertices by applying physical laws of charges and springs to the components of the graph. The resulting visualizations are evidence of the success of the approach as well as of the data sets that lend themselves to a clustering routine. Due to the visual product of the implementation, the algorithm is most useful as an aid in understanding the grouping pattern of a data set. Either for a rapid analysis or to assist in presentation, the visual result of the clustering approach is a useful tool for discovering trends in a data set.