The analysis of human activity data is an important research area in the context of ubiquitous and social environments. Using sensor data obtained by mobile devices, e.g., utilizing accelerometer sensors contained in mobile phones, behavioral patterns and models can then be obtained. However, often the utilized models are often not simple to interpret by humans in order to facilitate assessment, evaluation and validation, e. g., in medical contexts. In this paper, we investigate human activity recognition using class association rule mining. We propose a novel approach for generating interpretable rule sets for classification: We present an adaptive framework for mining class association rules using subgroup discovery, and analyze different techniques for obtaining the final classifier. For our evaluation, we apply real-world data collected for different activities using mobile phone sensors.