Multi-Tasks Discovery Method Based on the Concept Network for Data Mining

Abstract

Business and data understanding, that aims to identify multiple mining tasks, is the most primary phase in planning a practical data mining project. However, traditional tasks determination problem could only be solved by experienced analysts, which suffers from high communication cost and low efficiency. In this paper, we study the automatic task discovery method following the problem solving theory. First, we establish the concept network (CN) model to represent human knowledge and experience in the problem solving process. Then, we propose and demonstrate the structure of two major mining tasks (clustering and classification) in a CN. Finally, a data mining tasks discovery method (DMTD) is put forward, followed by two analysis subject evaluation algorithms. Experiment results illustrate that the DMTD is able to discover all the potential mining tasks from a predefined concept network, filtered by the important or interesting analysis subjects. Moreover, these tasks defined by the DMTD are proven to be available and valuable by fifty published papers.