Data Mining Technology across Academic Disciplines

.
DOI: 10.4236/iim.2011.32005   PDF   HTML     6,185 Downloads   11,709 Views   Citations

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.

Share and Cite:

L. Farmer, A. Safer and E. Chuk, "Data Mining Technology across Academic Disciplines," Intelligent Information Management, Vol. 3 No. 2, 2011, pp. 43-48. doi: 10.4236/iim.2011.32005.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Berry, M., and Linoff, G. 2004. Data Mining Techniques for Marking, Sales and Customer Support (2nd ed.). Wiley, New York.
[2] Duda, R., Hart, P., and Stork, D. 2000. Pattern Classification (2nd ed.). Wiley-Interscience, New York.
[3] 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
[4] Larose, D. 2005. Discovering Knowledge in Data. Wiley- Interscience, Hoboken, NJ.
[5] Olson, D. and Shi, Y. 2006. Introduction to Business Data Mining. McGraw-Hill, Columbus, OH.
[6] Roiger, R., and Geatz, M. 2003. Data Mining. Addison- Wesley, Boston.

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.