Title :
Measuring the quality of network visualization
Author_Institution :
Coll. of Inf. Sci. & Technol., Drexel Univ., Philadelphia, PA
Abstract :
A quantitative method is developed for measuring the quality of network visualizations in terms of log-likelihood metrics resulted from expectation maximization (EM) clustering intrinsic and extrinsic attributes of network nodes
Keywords :
data visualisation; learning (artificial intelligence); pattern clustering; expectation maximization clustering; log-likelihood metrics; network nodes; network visualizations; quality measurement; Chaos; Clustering algorithms; Data visualization; Educational institutions; Graphical user interfaces; Information science; Information systems; Machine learning; Machine learning algorithms; User interfaces; EM clustering; network visualization; quality metrics of quality;
Conference_Titel :
Digital Libraries, 2005. JCDL '05. Proceedings of the 5th ACM/IEEE-CS Joint Conference on
Conference_Location :
Denver, CO
Print_ISBN :
1-58113-876-8
DOI :
10.1145/1065385.1065509
