Computational Statistics

, Volume 19, Issue 1, pp 147–158 | Cite as

Hierarchical visual data mining for large-scale data

  • Matthew Ward
  • Wei Peng
  • Xiaoning Wang


An increasingly important problem in exploratory data analysis and visualization is that of scale; more and more data sets are much too large to analyze using traditional techniques, either in terms of the number of variables or the number of records. One approach to addressing this problem is the development and use of multiresolution strategies, where we represent the data at different levels of abstraction or detail through aggregation and summarization. In this paper we present an overview of our recent and current activities in the development of a multiresolution exploratory visualization environment for large-scale multivariate data. We have developed visualization, interaction, and data management techniques for effectively dealing with data sets that contain millions of records and/or hundreds of dimensions, and propose methods for applying similar approaches to extend the system to handle nominal as well as ordinal data.


Dimension Cluster Multiple Correspondence Analysis Information Visualization Interactive Exploration Dimension Hierarchy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Physica-Verlag 2004

Authors and Affiliations

  • Matthew Ward
    • 1
  • Wei Peng
    • 1
  • Xiaoning Wang
    • 1
  1. 1.Computer Science DepartmentWorcester Polytechnic InstituteWorcesterUSA

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