Real-Time Data Mining Methodology and a Supporting Framework

The need for real-time data mining has long been recognized in various application domains. However existing methodologies are still limited to the optimization of single classical data mining algorithms. In this paper, we investigate the development of a general purpose methodology for real-time data mining and propose a novel supporting framework. In the methodology, definition, characteristics and principles of real-time data mining are finely studied. The framework is proposed based on the novel dynamic data mining process model. The model offers the ability to incrementally update data mining knowledge and synchronously execute data mining tasks; an implementation of the framework and a case study are also presented.