An Associate-Rule-Aware Multidimensional Data Visualization Technique and Its Application to Painting Image Collections
Ayaka Kaneko, Akiko Komatsu, Takayuki Itoh, Florence Ying Wang · 2019
This paper presents a visualization technique for multidimensional datasets containing real and categorical variables. Supposing multidimensional datasets containing real and categorical values, this technique displays a set of axes corresponding to the dimensions of real values. The technique evenly divides the axes into several ranges and displays component bar charts there. It brightly draws the component bar charts if association rules are applied at the corresponding ranges of the dimensions of real values. As a result, this technique highlights association rules so that users can discover important relationships between real and categorical variables in multidimensional datasets. This paper introduces an application of the presented technique to painting image collections. This application visualizes image features and categorical information of painting images and provides a user interface to browse the painting images associated with the multidimensional values. This paper also introduces user evaluation results of the user interfaces for painting image collections.