Name
Abstract | ||
|---|---|---|
We propose a new method to project n-dimensional data onto two dimensions, for visualization purposes. Our goal is to produce a bi-dimensional representation that better separate existing clusters. Accordingly, to generate this projection we apply Differential Evolution as a meta-heuristic to optimize a divergence measure of the projected data. This divergence measure is based on the Cauchy-Schwartz divergence, extended for multiple classes. It accounts for the separability of the clusters in the projected space using the Renyi entropy and Information Theoretical Clustering analysis. We test the proposed method on two synthetic and five real world data sets, obtaining well separated projected clusters in two dimensions. These results were compared with results generated by PCA and a recent likelihood based visualization method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.ins.2012.12.041 | Inf. Sci. |
Keywords | Field | DocType |
visualization purpose,clustering visualization,real world data set,projected space,n-dimensional data,divergence measure,projected data,new method,cauchy-schwartz divergence,visualization method,optimized bi-dimensional data projection,evolutionary computation,pattern analysis,visualization | Cluster (physics),Data set,Divergence,Visualization,Computer science,Rényi entropy,Evolutionary computation,Differential evolution,Artificial intelligence,Cluster analysis,Machine learning | Journal |
Volume | ISSN | Citations |
232, | 0020-0255 | 2 |
PageRank | References | Authors |
0.39 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rodrigo T. Peres | 1 | 2 | 0.39 |
| Claus Aranha | 2 | 35 | 8.68 |
| Carlos E. Pedreira | 3 | 60 | 6.51 |