Multivariate Data Explanation by Jumping Emerging Patterns Visualization

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 the concept of Jumping Emerging Patterns to identify and aggregate several diversified patterns, producing explanations through logic combinations of data variables. The potential of VAX to interpret complex multivariate datasets is demonstrated through use cases employing two real-world datasets covering different scenarios.