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A Systemic Framework for the Field of Data Mining and Knowledge Discovery
Hong Kong, China December 18-December 22
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.24Sixth IEEE International Conference o ...
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Yi Peng, University of Nebraska at Omaha
Gang Kou, Thomson Legal & Regulatory, R&D, 610 Opperman Drive, Eagan, MN
Yong Shi, Graduate University of the Chinese Academy of Sciences, 100080, China
Zhengxin Chen, University of Nebraska at Omaha
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.
Citation:
Yi Peng, Gang Kou, Yong Shi, Zhengxin Chen, "A Systemic Framework for the Field of Data Mining and Knowledge Discovery," icdmw, pp.395-399, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
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