Enormous information in the Internet, especially in the health care field, makes users cannot get the information they want in time. To let medical staffs, even patients and users, absorb and understand information qu...
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ISBN:
(纸本)9781479987511
Enormous information in the Internet, especially in the health care field, makes users cannot get the information they want in time. To let medical staffs, even patients and users, absorb and understand information quickly, data visualization converts information to graphic style. However, some treemap algorithms are not suitable for displaying on certain conditions. This study proposes an improved algorithm, Innovated Squarified treemap (iSqT), to process the information by data visualization. A comparison shows that our iSqT algorithm provides better performance than original Squarified treemap algorithm. We expect this paper to facilitate the information be read easier by our iSqT approach in the rehabilitation field.
The visual analysis of large and complex relational data sets is a growing need in many application domains, such as social sciences, biology, computer networks, and software engineering. In this respect, the capabili...
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The visual analysis of large and complex relational data sets is a growing need in many application domains, such as social sciences, biology, computer networks, and software engineering. In this respect, the capability of quickly computing two-dimensional layouts of hierarchically clustered networks plays an important role and should be a major requirement of many graph visualization systems. We present algorithmic and experimental advances on the subject, namely: (i) we propose a new drawing algorithm that combines space-filling and fast force-directed methods;it runs in O(n log n + m) time, where n and m are the number of vertices and edges of the network, respectively. This running time does not depend on the number of clusters, thus the algorithm guarantees good time performance independently of the structure of the cluster hierarchy. As a further advantage, the algorithm can be easily parallelized. (ii) We discuss the results of an experimental analysis aimed at understanding which clustering algorithms can be used in combination with our visualization technique to generate better quality drawings for small-world and scale-free networks of medium and large size. As far as we know, no previous similar experiments have been done to this aim. (C) 2013 Elsevier Inc. All rights reserved.
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