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Hierarchical visual data mining for large-scale data

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Summary

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.

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Acknowledgements

This work has been supported by U.S. National Science Foundation under grants IIS-9732897, IIS-0119276, and EIA-9729878.

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Authors and Affiliations

  1. Computer Science Department, Worcester Polytechnic Institute, 01609, Worcester, MA, USA

    Matthew Ward, Wei Peng & Xiaoning Wang

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  1. Matthew Ward
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  2. Wei Peng
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  3. Xiaoning Wang
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Ward, M., Peng, W. & Wang, X. Hierarchical visual data mining for large-scale data. Computational Statistics 19, 147–158 (2004). https://doi.org/10.1007/BF02915281

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