DNA visual and analytic data mining
VIS '97 Proceedings of the 8th conference on Visualization '97
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Circle Graphs: New Visualization Tools for Text-Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
VizRank: Data Visualization Guided by Machine Learning
Data Mining and Knowledge Discovery
Cluster validity measurement for arbitrary shaped clusters
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Vectorized Radviz and Its Application to Multiple Cluster Datasets
IEEE Transactions on Visualization and Computer Graphics
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
IEEE Transactions on Pattern Analysis and Machine Intelligence
RadialViz: an orientation-free frequent pattern visualizer
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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The Radial Coordinate Visualization (Radviz) technique has been widely used to effectively evaluate the existence of patterns in highly dimensional data sets A crucial aspect of this technique lies in the arrangement of the dimensions, which determines the quality of the posterior visualization Dimension arrangement (DA) has been shown to be an NP-problem and different heuristics have been proposed to solve it using optimization techniques However, very little work has focused on understanding the relation between the arrangement of the dimensions and the quality of the visualization In this paper we first present two variations of the DA problem: (1) a Radviz independent approach and (2) a Radviz dependent approach We then describe the use of the Davies-Bouldin index to automatically evaluate the quality of a visualization i.e., its visual usefulness Our empirical evaluation is extensive and uses both real and synthetic data sets in order to evaluate our proposed methods and to fully understand the impact that parameters such as number of samples, dimensions, or cluster separability have in the relation between the optimization algorithm and the visualization tool.