Visual content correlation analysis

Authors:
Furu Wei;Lei Shi;Li Tan;Xiaohua Sun;Xiaoxiao Lian;Shixia Liu;Michelle X. Zhou
Affiliations:
IBM Research, Beijing, China;IBM Research, Beijing, China;IBM Research, Beijing, China;IBM Research, Beijing, China;IBM Research, Beijing, China;IBM Research, Beijing, China;IBM Research, Hawthorn, NY, USA
Venue:
Proceedings of the first international workshop on Intelligent visual interfaces for text analysis
Year:
2010

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Abstract

Correlating content from multiple data fields is one of the key challenges in text mining. In this paper, we propose a visual analytics approach that leverages both content correlation analysis and interactive visualization technologies in analyzing and understanding content correlations. We have applied our work to analyzing NHAMCS data (National Hospital Ambulatory Medical Care Survey), which helps reveal healthcare-related data patterns through the correlations between unstructured data fields (e.g., cause of injury and diagnosis) and between structured and unstructured fields (e.g., gender and cause of injury).