Multivariate Data Explanation by Jumping Emerging Patterns Visualization
M\'ario Popolin Neto, Fernando V. Paulovich

TL;DR
This paper introduces VAX, a visual analytics method that uses Jumping Emerging Patterns to identify and visualize complex multivariate data patterns, enhancing interpretability beyond simple models.
Contribution
VAX is the first VA approach to leverage Jumping Emerging Patterns for visualizing and interpreting complex multivariate data patterns in a comprehensive manner.
Findings
VAX effectively visualizes intricate data patterns.
It outperforms simpler models in capturing data complexity.
Use cases demonstrate its practical applicability.
Abstract
Visual Analytics (VA) tools and techniques have been instrumental in supporting users to build better classification models, interpret models' overall logic, and audit results. In a different direction, VA has recently been applied to transform classification models into descriptive mechanisms instead of predictive. The idea is to use such models as surrogates for data patterns, visualizing the model to understand the phenomenon represented by the data. Although very useful and inspiring, the few proposed approaches have opted to use low complex classification models to promote straightforward interpretation, presenting limitations to capture intricate data patterns. In this paper, we present VAX (multiVariate dAta eXplanation), a new VA method to support the identification and visual interpretation of patterns in multivariate datasets. Unlike the existing similar approaches, VAX uses…
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Taxonomy
TopicsData Visualization and Analytics · Time Series Analysis and Forecasting · Data Analysis with R
