A Systemic Framework for the Field of Data Mining and Knowledge Discovery

Authors:
Yi Peng;Gang Kou;Yong Shi;Zhengxin Chen
Affiliations:
University of Nebraska at Omaha;Thomson Legal & Regulatory, R&D, 610 Opperman Drive, Eagan, MN;Graduate University of the Chinese Academy of Sciences, 100080, China;University of Nebraska at Omaha
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

This paper proposes a systemic framework that attempts to define the domain and major areas of Data Mining and Knowledge Discovery (DMKD). Grounded theory approach, a qualitative method that inductively develops an understanding of phenomena, is adopted to build the framework. Using a large collection of DMKD literature, including DMKD journals, conference proceedings, syllabuses, and dissertations, this study develops a framework of eight main areas for the field: (1) foundations of DMKD, (2) learning methods & techniques, (3) mining complex data, (4) highperformance & distributed data mining, (5) data mining software & systems, (6) data mining process & project, (7) data mining applications, (8) data mining tasks. The last area is suggested as the central theme of the field. Keywords: Data mining and knowledge discovery, Grounded theory, Theoretic framework.