Hypothesis generation and data quality assessment through association mining

Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. In this paper we present a semantic network based association analysis model including three spreading activation methods, and apply this model to assess the quality of a dataset, and generate semantically valid new hypotheses for further investigation. We evaluate our approach on a real public health dataset, the Heartfelt study, and the experiment shows promising results.