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.

Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e.g., from overview to details). To illustrate our approach, two usage examples are presented.

In this work, we introduce SentiCompass for exploring and comparing the sentiments of time-varying Twitter data. Our visualization design combines 2D psychology model of affect (i.e. emotion) with a time tunnel representation. To illustrate our visualization design, two case studies are conducted. They demonstrate the effectiveness of SentiCompass in achieving various tasks related to temporal sentiment and affective analysis of tweets. The interactive demo of our system is available at: http://youtu.be/ZaMF6VNO7tA

In cartographic symbology, the evaluation of a symbol set's visual similarity is an important and frequently required task. Usually, after a set of map symbols are designed, the visual similarity of symbols need to be manually examined. To fully automate this task, in this paper, we propose two approaches based on entropy calculated on SOM surface for quantifying and visualizing the visual similarities of cartographic symbol sets. Using our approaches, the visual similarities of multiple symbol sets can be compared efficiently. Apart from evaluating the visual similarities of cartographic symbol sets, both of our methods are also suitable for applications where comparing the visual similarities of multiple image datasets are required.

Painting collections from the old masters are valuable cultural heritage of human history. Their artistic styles can be generally determined by their art periods. From analyzing and visualizing the relationships of different artistic styles, information can be found to facilitate art history studies. In this paper, we propose a Self-organizing Map (SOM) based framework specifically for analyzing and visualizing the relationships among painting collections from artistic perspectives. In our framework, we first define a set of image features based on artistic concepts used in art criticism; then a SOM-based hierarchical model is used to analyze features extracted from individual artists' painting collections. For our experiments, we obtain painting collections of six painting masters representing three art movements: post-impressionism, cubism and renaissance. An interactive web interface is also built to present our artistic influence analysis results. Through our experimental results, styles of different painting collections and their influential relationships can be analyzed and visualized from artistic perspectives.