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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