Vectorized Radviz and Its Application to Multiple Cluster Datasets

Radviz is a radial visualization with dimensions assigned to points called dimensional anchors (DAs) placed on the circumference of a circle. Records are assigned locations within the circle as a function of its relative attraction to each of the DAs. The DAs can be moved either interactively or algorithmically to reveal different meaningful patterns in the dataset. In this paper we describe Vectorized Radviz (VRV) which extends the number of dimensions through data flattening. We show how VRV increases the power of Radviz through these extra dimensions by enhancing the flexibility in the layout of the DAs. We apply VRV to the problem of analyzing the results of multiple clusterings of the same data set, called multiple cluster sets or cluster ensembles. We show how features of VRV help discern patterns across the multiple cluster sets. We use the Iris data set to explain VRV and a newt gene microarray data set used in studying limb regeneration to show its utility. We then discuss further applications of VRV.

[1]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[2]  H. Charles Romesburg,et al.  Cluster analysis for researchers , 1984 .

[3]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[4]  Georges G. Grinstein,et al.  DNA visual and analytic data mining , 1997, Proceedings. Visualization '97 (Cat. No. 97CB36155).

[5]  Georges G. Grinstein,et al.  DNA visual and analytic data mining , 1997 .

[6]  J. C. BurgesChristopher.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[7]  Stefan Berchtold,et al.  Similarity clustering of dimensions for an enhanced visualization of multidimensional data , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[8]  Georges G. Grinstein,et al.  Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations , 1999, NPIVM '99.

[9]  김삼묘,et al.  “Bioinformatics” 특집을 내면서 , 2000 .

[10]  Eser Kandogan,et al.  Visualizing multi-dimensional clusters, trends, and outliers using star coordinates , 2001, KDD '01.

[11]  U. Fayyad,et al.  Information Visualization in Data Mining and Knowledge Discovery , 2001 .

[12]  Ana L. N. Fred,et al.  Finding Consistent Clusters in Data Partitions , 2001, Multiple Classifier Systems.

[13]  Andreas Wierse,et al.  Information Visualization in Data Mining and Knowledge Discovery , 2001 .

[14]  Georges G. Grinstein,et al.  High-Dimensional Visualization Support for Data Mining Gene Expression Data , 2001 .

[15]  Javier M. Moguerza,et al.  Detecting the Number of Clusters Using a Support Vector Machine Approach , 2002, ICANN.

[16]  S. Rüger,et al.  Navigator : A Visualization Tool for Document Searching and Browsing , 2003 .

[17]  Matthew O. Ward,et al.  Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering , 2004 .

[18]  Aidong Zhang,et al.  VizStruct: exploratory visualization for gene expression profiling , 2004, Bioinform..

[19]  K. Marx,et al.  Applications of Machine Learning and High‐Dimensional Visualization in Cancer Detection, Diagnosis, and Management , 2004, Annals of the New York Academy of Sciences.

[20]  Matthew O. Ward,et al.  Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering , 2004, IEEE Symposium on Information Visualization.

[21]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[22]  Ana L. N. Fred,et al.  Combining multiple clusterings using evidence accumulation , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  G. Santucci,et al.  SpringView: cooperation of radviz and parallel coordinates for view optimization and clutter reduction , 2005, Coordinated and Multiple Views in Exploratory Visualization (CMV'05).

[24]  Ivan Bratko,et al.  VizRank: finding informative data projections in functional genomics by machine learning , 2005, Bioinform..

[25]  Georges G. Grinstein,et al.  Universal visualization platform , 2005, IS&T/SPIE Electronic Imaging.

[26]  Blaz Zupan,et al.  FreeViz - An intelligent multivariate visualization approach to explorative analysis of biomedical data , 2007, J. Biomed. Informatics.

[27]  ML Ujwal,et al.  A Machine Learning Approach to Pharmacological Profiling of the Quinone Scaffold in the NCI Database: A Compound Class Enriched in Those Effective Against Melanoma and Leukemia Cell Lines , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[28]  Georges G. Grinstein,et al.  Evidence for Proximal to Distal Appendage Amputation Site Effects from Global Gene Expression Correlations Found in Newt Microarrays , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[29]  André Csillaghy,et al.  SphereViz - Data Exploration in a Virtual Reality Environment , 2007, 2007 11th International Conference Information Visualization (IV '07).

[30]  Georges G. Grinstein,et al.  Heat Map Visualizations Allow Comparison of Multiple Clustering Results and Evaluation of Dataset Quality: Application to Microarray Data , 2007, 2007 11th International Conference Information Visualization (IV '07).