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Suitable reference marks are an important part of creating an understandable visualization. The reference marks create the frame in which the data is understood, thereby preserving the context of the data and allowing the transition from data to information to be made. However, reference marks (including legends, axial and point labels) are given insufficient attention in many visualization frameworks...
Existing Information Visualization models provide insufficient support to visualization programmers in creating applications. They either broad and taxonomy based, or narrowly focused on isolated aspects of the visualization problem. Taxonomy based tools are good for categorizing what has been or needs to be done, but provide little help in accomplishing those goals. Narrowly focused models allow...
Visual similarity matrices (VSMs) are a common technique for visualizing graphs and other types of relational data. While traditionally used for small data sets or well-ordered large data sets, they have recently become popular for visualizing large graphs. However, our experience with users has revealed that large VSMs are difficult to interpret. In this paper, we catalog common structural features...
In this study, we examine the use of graph ordering algorithms for visual analysis of data sets using visual similarity matrices. Visual similarity matrices display the relationships between data items in a dot-matrix plot format, with the axes labeled with the data items and points drawn if there is a relationship between two data items. The biggest challenge for displaying data using this representation...
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