Visualizing unstructured text sequences using iterative visual clustering

Authors:
Qian You;Shiaofen Fang;Patricia Ebright
Affiliations:
Department of Computer and Information Science, Indiana University- Purdue University, Indianapolis;Department of Computer and Information Science, Indiana University- Purdue University, Indianapolis;Department of Adult Health, School of Nursing, Indiana University, Indianapolis, IN
Venue:
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Year:
2007

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Abstract

This paper presents a keyword-based information visualization technique for unstructured text sequences. The text sequence data comes from nursing narratives records, which are mostly text fragments with incomplete and unreliable grammatical structures. Proper visualization of such text sequences can reveal patterns and trend information rooted in the text records, and has significant applications in many fields such as medical informatics and text mining. In this paper, an Iterative Visual Clustering (IVC) technique is developed to facilitate multi-scale visualization, and at the same time provide abstraction and knowledge discovery functionalities at the visualization level. Interactive visualization and user feedbacks are used to iteratively group keywords to form higher level concepts and keyword clusters, which are then feedback to the visualization process for evaluation and pattern discovery. Distribution curves of keywords and their clusters are visualized at various scales under Gaussian smoothing to search for meaningful patterns and concepts.