Association Analysis for Visual Exploration of Multivariate Scientific Data Sets


The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it needs investigating the sometimes hidden associations between completely different variables and specific scalar values to understand the information's multi-faceted properties. During this paper, we have a tendency to present a novel association analysis methodology that guides visual exploration of scalar-level associations in the multivariate context. We have a tendency to model the directional interactions between scalars of different variables as info flows primarily based on association rules. We introduce the ideas of informativeness and uniqueness to explain how information flows between scalars of various variables and the way they're associated with each other within the multivariate domain. Based mostly on scalar-level associations represented by a probabilistic association graph, we tend to propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to judge the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with assured associations within the multivariate spatial domain, and offer guidelines for visual exploration using our framework. We tend to demonstrate the effectiveness and usefulness of our approach through case studies using 3 representative multivariate scientific data sets.

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