Data Mining and Knowledge Discovery

, Volume 13, Issue 2, pp 119–136

VizRank: Data Visualization Guided by Machine Learning

Authors

    • Faculty of Computer and Information ScienceUniversity of Ljubljana
  • Blaž Zupan
    • Faculty of Computer and Information ScienceUniversity of Ljubljana
    • Department of Molecular and Human GeneticsBaylor College of Medicine
  • Gaj Vidmar
    • Institute of Biomedical InformaticsUniversity of Ljubljana
  • Ivan Bratko
    • Faculty of Computer and Information ScienceUniversity of Ljubljana
    • Jozef Stefan Institute
Article

DOI: 10.1007/s10618-005-0031-5

Cite this article as:
Leban, G., Zupan, B., Vidmar, G. et al. Data Min Knowl Disc (2006) 13: 119. doi:10.1007/s10618-005-0031-5

Abstract

Data visualization plays a crucial role in identifying interesting patterns in exploratory data analysis. Its use is, however, made difficult by the large number of possible data projections showing different attribute subsets that must be evaluated by the data analyst. In this paper, we introduce a method called VizRank, which is applied on classified data to automatically select the most useful data projections. VizRank can be used with any visualization method that maps attribute values to points in a two-dimensional visualization space. It assesses possible data projections and ranks them by their ability to visually discriminate between classes. The quality of class separation is estimated by computing the predictive accuracy of k-nearest neighbor classifier on the data set consisting of x and y positions of the projected data points and their class information. The paper introduces the method and presents experimental results which show that VizRank's ranking of projections highly agrees with subjective rankings by data analysts. The practical use of VizRank is also demonstrated by an application in the field of functional genomics.

Keywords

data visualizationdata miningvisual data miningmachine learningexploratory data analysis

Copyright information

© Springer Science + Business Media, LLC 2006