Data Mining and Knowledge Discovery

, Volume 13, Issue 2, pp 119–136

VizRank: Data Visualization Guided by Machine Learning

  • Gregor Leban
  • Blaž Zupan
  • Gaj Vidmar
  • Ivan Bratko
Article

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 visualization data mining visual data mining machine learning exploratory data analysis 

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Gregor Leban
    • 1
  • Blaž Zupan
    • 1
    • 2
  • Gaj Vidmar
    • 3
  • Ivan Bratko
    • 1
    • 4
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Department of Molecular and Human GeneticsBaylor College of MedicineHoustonUSA
  3. 3.Institute of Biomedical InformaticsUniversity of LjubljanaLjubljanaSlovenia
  4. 4.Jozef Stefan InstituteLjubljanaSlovenia

Personalised recommendations