Literature DB >> 20694164

Multivariate Visual Explanation for High Dimensional Datasets.

Scott Barlowe1, Tianyi Zhang, Yujie Liu, Jing Yang, Donald Jacobs.   

Abstract

Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach.

Entities:  

Year:  2008        PMID: 20694164      PMCID: PMC2916668          DOI: 10.1109/VAST.2008.4677368

Source DB:  PubMed          Journal:  Proc IEEE Symp Vis Anal Sci Technol


  2 in total

1.  Response-surface analysis of exposure-duration relationships: the effects of hyperthermia on embryonic development of the rat in vitro.

Authors:  G L Kimmel; P L Williams; T W Claggett; C A Kimmel
Journal:  Toxicol Sci       Date:  2002-10       Impact factor: 4.849

Review 2.  Mathematical modeling as a tool for investigating cell cycle control networks.

Authors:  Jill C Sible; John J Tyson
Journal:  Methods       Date:  2007-02       Impact factor: 3.608

  2 in total

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