Abstract
In this paper, it is argued that although data mining (DM) is being touted as a solution to many business problems and is basking in the glory of electronic business environments of today, as practiced currently, it reflects a preoccupation with short-run commercial applications and a neglect of the underlying theoretical issues. Although an argument can be made that theoretical precedence is not a necessary prerequisite for practical application or for commercial success, it can also be argued that an exclusive reliance on data-driven and exploratory components of pattern recognition without a corresponding attention to the causal schemas underlying patterns is destined to limit the potential for DM to evolve into a long-term solution to business problems or into an intellectual discipline in its own right. This paper presents an overview of key features and assumptions in DM and examines some of the key practical, philosophical, and foundational issues in DM.
Similar content being viewed by others
References
Bigus JP (1996) Data mining with neural networks: solving business problems—from application development to decision support. McGraw-Hill, New York
Brown JR (2001) Who rules in science?. Harvard University Press, Cambridge
Danna A, Gandy OH Jr (2002) All that glitter is not gold: digging beneath the surface of data mining. J Bus Ethics 40:373–386
Friedman JH (1997) Data mining and statistics: what’s the connection? Computing science and statistics. In: Proceedings of the 29th international symposium on the interface, pp 3–9. http://www-stat.stanford.edu/∼jhf/#reports
Gatnar F (2002) What is data mining? Stat Transit 5(5):837–842
Harrold D (2000) What’s your data telling you?. Control Eng 47(3):9–12
Hempel CG (1966) Philosophy of natural science. Prentice-Hall, Englewood Cliffs
Hunt SD (1983) Marketing theory: the philosophy of marketing science. Irwin, Homewood
Korukonda AR (1989) Mixing levels of analysis in organizational research. Can J Admin Sci 6(2):1–8
LeBeau C (2000) Mountains to mine. Am Demogr 22(8):40
Naisbitt J (1982) Megatrends: ten new directions transforming our lives. Warner Books, New York
Neurath O, Hahn H, Carnap R (1973) The scientific conception of the world: the Vienna Circle. In: Neurath M, Robert SC (eds) Empiricism and sociology with a selection of biographical and autobiographical sketches. Reidel, Dordrecht, pp 301–318
Ponnaiah P (2001) Data warehousing fundamentals: a comprehensive guide for IT professionals. Wiley, New York
Rob P, Coronel C (2000) Database systems: design, implementation, management. Course Technology, Cambridge
Supermarket Business (2000) Load 16 tons of data. Supermarket Business, August 15, p3. http://www.grocerynetwork.com
Taschek J (2001) Data mining’s dead: long live analytics. eWeek 18(45):54–55
Time (2003) Numbers: 4.5%: increase in sales at Red Lobster restaurant during March in spite (or because) of the war in Iraq. Time, April 28, p 25
Yadav SB, Korukonda AR (1985) Management of type III error in problem identification. Interfaces 15(4):55–61
Wellman D (1999) Down in the (data) mines. Supermarket Business May, pp 3–36
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Korukonda, A.R. Technique without theory or theory from technique? An examination of practical, philosophical, and foundational issues in data mining. AI & Soc 21, 347–355 (2007). https://doi.org/10.1007/s00146-006-0064-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00146-006-0064-3