Extracting the characteristic of residential energy consumption is a key factor that allows load aggregators (LAs) and energy-retailers (ERs) to make intelligent assessment about conserving energy and promoting the flexibility of residential demand response. This paper presents a novel data mining framework for the analysis of the regional demand-side management. Then, this paper investigates the application and effectiveness of several data mining approaches to compare and identify the characteristics among hundreds of residential customers. In addition, an efficient characteristic-recognition model is proposed for characteristic-classification to define the potential of residential demand response. Finally, the proposed data mining framework is tested on a large-scale residential database. Results of case study demonstrate the effectiveness of the proposed approach for demand-side management.