Improved visual correlation analysis for multidimensional data. (August 2017)

• Record Type: Journal Article
• Authors: Zhang, Yi; Liu, Teng; Li, Kefei; Zhang, Jiawan
• Is Part Of: Journal of visual languages & computing. Volume 41(2017)
• Page Start: 121
• Page End: 132
• Publication Date: 2017-08
• Subjects: Multidimensional visualization -- Correlation analysis -- Data distribution feature -- Dimension reordering; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.03.005
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: With the era of data explosion coming, multidimensional visualization, as one of the most helpful data analysis technologies, is more frequently applied to the tasks of multidimensional data analysis. Correlation analysis is an efficient technique to reveal the complex relationships existing among the dimensions in multidimensional data. However, for the multidimensional data with complex dimension features, traditional correlation analysis methods are inaccurate and limited. In this paper, we introduce the improved Pearson correlation coefficient and mutual information correlation analysis respectively to detect the dimensions' linear and non-linear correlations. For the linear case, all dimensions are classified into three groups according to their distributions. Then we correspondingly select the appropriate parameters for each group of dimensions to calculate their correlations. For the non-linear case, we cluster the data within each dimension. Then their probability distributions are calculated to analyze the dimensions' correlations and dependencies based on the mutual information correlation analysis. Finally, we use the relationships between dimensions as the criteria for interactive ordering of axes in parallel coordinate displays.