Mining hidden correlations between sleep and lifestyle factors from quantified-self data

It has been widely recognized that discovering potential contributing factors to personal sleep is as important as understanding sleep pattern per se. However, in large quantified-self datasets, contributing factors may only show correlations to sleep when their values are within certain ranges. Existing correlation analysis using Pearson Correlation Coefficient cannot identify such hidden dependencies. We propose a new method based on association rules mining. Our method not only can discover hidden correlations that existing methods cannot, but also provides users with actionable knowledge to guide sleep improvement through lifestyle change.