Iterative Visual Clustering for Learning Concepts from Unstructured Text
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Qian You,Shiaofen Fang,Patricia Ebright. Iterative Visual Clustering for Learning Concepts from Unstructured Text. International Journal of Software and Informatics, 2012,6(1):43~59
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Abstract:Discovering concepts from vast amount of text is is an important but hard explorative task. A common approach is to identify meaningful keyword clusters with interesting temporal distributive trends from unstructured text. However, usually lacking clearly de ned objective functions, users' domain knowledge and interactions need to be feed back and to drive the clustering process. Therefore we propose the iterative visual clustering (IVC), a noval visual text analytical model. It uses di erent types of visualizations to help users cluster keywords interactively, as well as uses graphics to suggest good clustering options. The most distinctive di erence between IVC and traditional text analytical tools is, IVC has a formal on-line learning model which learns users' preference iteratively: during iterations, IVC transforms users' interactions into model training input, and then visualizes the output for more users interactions. We apply IVC as a visual text mining framework to extract concepts from nursing narratives. With the engagement of domain knowledge, IVC has achieved insightful concepts with interesting patterns.
keywords:text and document visualization  iterative visual analytics  nursing data processing
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