Research ArticleOpen AccessGoogle Scholar indexed
Data Mining Technology across Academic Disciplines
- 1
- 2
- 3
Intelligent Information Management·Volume 03 (2011)·Pages 43–48·Published 9 March 2011·DOI10.4236/iim.2011.32005
Copy link · social · email
Abstract
University courses in data mining across the United States are taught primarily in departments of business, computer science/engineering, statistics, and library/information science. Faculty in each of these departments teach data mining with a unique emphasis, although there is considerable overlap relative to course offerings, terminology, technology, resources, and faculty publications. Content analysis research aims to describe in detail the range of data mining technology differences and overlap across academic disciplines.
KeywordsData MiningStatisticsAcademics
- Berry, M., and Linoff, G. 2004. Data Mining Techniques for Marking, Sales and Customer Support (2nd ed.). Wiley, New York.
- Duda, R., Hart, P., and Stork, D. 2000. Pattern Classification (2nd ed.). Wiley-Interscience, New York.
- Hastie, T., Tibshirani, R., and Friedman, J. 2009. The Elements of Statistical Learning (2nd ed.). Springer. New York. doi:10.1007/978-0-387-84858-7
- Larose, D. 2005. Discovering Knowledge in Data. Wiley- Interscience, Hoboken, NJ.
- Olson, D. and Shi, Y. 2006. Introduction to Business Data Mining. McGraw-Hill, Columbus, OH.
- Roiger, R., and Geatz, M. 2003. Data Mining. Addison- Wesley, Boston.