VizRank: Data Visualization Guided by Machine Learning View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-09

AUTHORS

Gregor Leban, Blaž Zupan, Gaj Vidmar, Ivan Bratko

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. More... »

PAGES

119-136

References to SciGraph publications

  • 1995. Induction of Decision Trees Using Relieff in PROCEEDINGS OF THE ISSEK94 WORKSHOP ON MATHEMATICAL AND STATISTICAL METHODS IN ARTIFICIAL INTELLIGENCE
  • 1973-06. A rank test for two group concordance in PSYCHOMETRIKA
  • 2001. The Elements of Statistical Learning, Data Mining, Inference, and Prediction in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10618-005-0031-5

    DOI

    http://dx.doi.org/10.1007/s10618-005-0031-5

    DIMENSIONS

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