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Decomposition and Simplification of Multivariate Data using Pareto Sets | IEEE Journals & Magazine | IEEE Xplore
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Decomposition and Simplification of Multivariate Data using Pareto Sets


Abstract:

Topological and structural analysis of multivariate data is aimed at improving the understanding and usage of such data through identification of intrinsic features and s...Show More

Abstract:

Topological and structural analysis of multivariate data is aimed at improving the understanding and usage of such data through identification of intrinsic features and structural relationships among multiple variables. We present two novel methods for simplifying so-called Pareto sets that describe such structural relationships. Such simplification is a precondition for meaningful visualization of structurally rich or noisy data. As a framework for simplification operations, we introduce a decomposition of the data domain into regions of equivalent structural behavior and the reachability graph that describes global connectivity of Pareto extrema. Simplification is then performed as a sequence of edge collapses in this graph; to determine a suitable sequence of such operations, we describe and utilize a comparison measure that reflects the changes to the data that each operation represents. We demonstrate and evaluate our methods on synthetic and real-world examples.
Published in: IEEE Transactions on Visualization and Computer Graphics ( Volume: 20, Issue: 12, 31 December 2014)
Page(s): 2684 - 2693
Date of Publication: 06 November 2014

ISSN Information:

PubMed ID: 26356982

Funding Agency:


References

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