A descriptive framework for the field of data mining and knowledge discovery

As our abilities to collect and store various types of datasets are continually increasing, the demands for advanced techniques and tools to understand and make use of these large data keep growing. No single existing field is capable of satisfying the needs. Data Mining and Knowledge Discovery (DMKD), which utilizes methods, techniques, and tools from diverse disciplines, emerged in last decade to solve this problem. It brings knowledge and theories from several fields including databases, machine learning, optimization, statistics, and data visualization and has been applied to various real-life applications. Even though data mining has made significant progress during the past fifteen years, most research effort is devoted to developing effective and efficient algorithms that can extract knowledge from data and not enough attention has been paid to the philosophical foundations of data mining. The objective of this research is to provide a conceptual framework that identifies major research areas of data mining and knowledge discovery (DMKD) for students and beginners and describe the longitudinal changes of DMKD research activities. Using the textual documents collected from premier DMKD journals, conference proceedings, syllabi, and dissertations, this study is intended to address the following issues: What are the major subjects of this field? What is the central theme? What are the connections among these subjects? What are the longitudinal changes of DMKD research? To answer these questions, this research uses a combination of grounded theory and document clustering. The result will represent previous and current DMKD research activities in the form of a framework. The resulting framework should allow people to comprehend the entire domain of DMKD research and assist identification of areas in need of more research efforts.