Shape-embedded-histograms for visual data mining

Authors:
Amihood Amir;Reuven Kashi;Daniel A. Keim;Nathan S. Netanyahu;Markus Wawryniuk
Affiliations:
Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel;Rutgers Center for Operations Research, Rutgers, The State University of New Jersey, Piscataway, NJ;Department of Computer & Information Science, University of Konstanz, Konstanz, Germany;Center for Automation Research, University of Maryland, College Park, MD;Department of Computer & Information Science, University of Konstanz, Konstanz, Germany
Venue:
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
Year:
2004

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Abstract

Scatterplots are widely used in exploratory data analysis and class visualization. The advantages of scatterplots are that they are easy to understand and allow the user to draw conclusions about the attributes which span the projection screen. Unfortunately, scatterplots have the overplotting problem which is especially critical when high-dimensional data are mapped to low-dimensional visualizations. Overplotting makes it hard to detect the structure in the data, such as dependencies or areas of high density. In this paper we show that by extending the concept of Pixel Validity (1) the problem of overplotting or occlusion can be avoided and (2) the user has the possibility to see information about an additional third variable. In our extension of the Pixel Validity concept, we summarize the data which are projected onto a given region by generating a histogram over the required attribute. This is then embedded in the visualization by a pixel-based technique.