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Adaptive Study Design Through Semantic Association Rule Analysis

Adaptive Study Design Through Semantic Association Rule Analysis

Ping Chen, Wei Ding, Walter Garcia
Copyright: © 2011 |Volume: 3 |Issue: 2 |Pages: 15
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781613509180|DOI: 10.4018/jssci.2011040103
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MLA

Chen, Ping, et al. "Adaptive Study Design Through Semantic Association Rule Analysis." IJSSCI vol.3, no.2 2011: pp.34-48. http://doi.org/10.4018/jssci.2011040103

APA

Chen, P., Ding, W., & Garcia, W. (2011). Adaptive Study Design Through Semantic Association Rule Analysis. International Journal of Software Science and Computational Intelligence (IJSSCI), 3(2), 34-48. http://doi.org/10.4018/jssci.2011040103

Chicago

Chen, Ping, Wei Ding, and Walter Garcia. "Adaptive Study Design Through Semantic Association Rule Analysis," International Journal of Software Science and Computational Intelligence (IJSSCI) 3, no.2: 34-48. http://doi.org/10.4018/jssci.2011040103

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Abstract

Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. This paper presents a semantic network-based association analysis model including three spreading activation methods. It applies this model to assess the quality of a dataset, and generate semantically valid new hypotheses for adaptive study design especially useful in medical studies. The approach is evaluated on a real public health dataset, the Heartfelt study, and the experiment shows promising results.

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