Parallel Sets Based on the Improved ACLEARCR Algorithm

Abstract:

Classic part-labeled data visualization method Parallel Sets is applied to represent visualization of multivariate data with measures. Currently, in all walks of life emerge abundant small records and documents. Thus to sort the massive categorical measures of small value on the variable axis plays an important role in reducing clutters in the view. This paper is based on the Advanced Categories Layout basEd on Average heuRistic with Cardinality Reduction (ACLEARCR), and proposes an optimization sequence algorithm aiming at categorical measures of small value. The case study proves that the proposed method is effective.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2318-2322
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2318
• Online since: July 2014
• Authors: Hai Kuo Zhang*, Ning Wei Sun, Chong Zhang, Ting Ting Jiang, Chang Hua Dai
• Keywords: Clutter Reduction, Corresponding Degree for Categorical Measures, Measurement Ratio, Multivariate Data Visualization, Parallel Sets

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