Abstract
Abstract Treemaps are well-known for visualizing hierarchical data. Most related approaches have been focused on layout algorithms and paid little attention to other display properties and interactions. Furthermore, the structural information in conventional Treemaps is too implicit for viewers to perceive. This paper presents Cabinet Tree, an approach that: i) draws branches explicitly to show relational structures, ii) adapts a space-optimized layout for leaves and maximizes the space utilization, iii) uses coloring and labeling strategies to clearly reveal patterns and contrast different attributes intuitively. We also apply the continuous node selection and detail window techniques to support user interaction with different levels of the hierarchies. Our quantitative evaluations demonstrate that Cabinet Tree achieves good scalability for increased resolutions and big datasets.
Highlights
Much of data we use today has a hierarchical structure
A capability of visualizing the entire structure while supporting deep exploration at different levels of granularity is urgently needed for effective knowledge discovery [3]
The Treemap algorithm ensures almost 100 % use of the space by dividing it into a nested sequence of rectangles whose areas correspond to an attribute of the dataset, effectively combining features of a Venn diagram and a pie chart [6]
Summary
Much of data we use today has a hierarchical structure. Examples of hierarchical structures include university-department structure, family tree, library catalogues and so on. The use of other display properties (e.g. color, label) is important for an intuitive visualization and efficient interaction techniques are necessary for navigating large Treemap to view details. This paper presents a space-filling technique, called Cabinet Tree, for visualizing big hierarchical data. Layout Treemap was first proposed by Johnson and Shneiderman in 1991, called Slice and Dice Treemap (S&D Treemap for short) [4] It divides the full display space into a nested sequence of rectangles recursively in an interleaved horizontal-vertical manner to provide compact views. Main properties interleaved horizontal-vertical good space utilization and aspect ratios, but lost ordering partially ordered completely ordered better spatial continuity polygons are used to enhance the visual presentation suitable for unit based inputs the underlying flow contour pattern to guide the placement of nodes to gain a better spatial continuity [19]. The layout algorithm is outlined in Algorithm 1, that is conceptually recursive but implemented iteratively: Algorithm 1 Partitioning
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