In this paper we analyze pricing and capacity allocation decisions in a simple two-class airline revenue management scenario using data mining techniques. Our study is based on a lab experiment with a 2x2 between subjects design We consider four treatments based on class-1 price being low or high and whether the pricing decision is made solely or jointly along with the capacity allocation decision. We study the connection between experiment performance and personal factors such as cognitive reflection, maximizing/satisficing tendency, risk-aversion and loss aversion, as well as demographic information such as gender and education background. We use data mining techniques such as feature selection and cluster analysis.