Ideal Modelling of Data Mining

The study of data mining has focused primarily on the mining algorithms and their applications, while relies its foundations on established fields, such as logic statistical analysis, machine learning, database. Motivated by the practical needs of specific types of real world data analysis problems, many mining algorithms are designed and studied. They include association rule mining, classification rule mining, exception and peculiarity rule mining, sequence mining, stream mining, text mining, web mining, and others. A review of data mining literature suggests that there does not exist a well-accepted and non-controversial conceptual framework. A lack of conceptual modelling may jeopardize further development of data mining. Our discussions are unique and differ from existing studies in several perspectives. First, we treat data mining as a field of study and emphasize the study of the nature, the scope, and philosophical foundations of data mining. We stress on the understanding of data mining as a scientific inquiry, in addition to simply empirical investigations. We pay more attention to the effectiveness of data mining methods, rather than only to the efficiency. Second, we view data mining in a wide context of scientific research, in terms of their goals, processes and methods. Third, we search for a unified and general framework, or at least general principles and guidelines, rather than a family of isolated algorithms. The framework aims at finding answers to what and why questions, as well as how questions. Forth, with the help of conceptual modelling, we attempt to move beyond trial and error, or ad hoc, applications of data mining algorithms, which dominate most current applied studies of data mining.