Abstract
Recent years have seen the emergence of data analytic techniques requiring for their practical use previously unimaginable raw computational power. Such techniques include neural network analysis, genetic algorithms, classification and regression trees, v-fold cross-validation clustering and suchlike. Many of these methods are what could be called ‘learning’ algorithms, which can be used for prediction, classification, association, and clustering of data based on previously estimated features of a data set. In other words, they are ‘trained’ on a data set with both predictors and target variables, and the model estimated is then used on future data which does not contain measured values of the target variable. Or in clustering methods, an iterative algorithm looks to generate clusters which are as homogenous within and as heterogeneous between as possible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Adriaans, P., Zantinge, D.: Data Mining. Addison-Wesley, Harlow, England (1996)
Anderson, C.: The End of Science: The Data Deluge Makes the Scientific Method Obsolete. In: WIRED, vol. 16 (7), pp. 108–109 (2008)
Buzzell, R.D.: Is Marketing a Science? Harvard Business Review 41(1), 32–40 (1963)
Brown, S.: Postmodern Marketing, Thompson, London (1995)
Feyerabend, P.K.: Against Method, 3rd edn. Verso, London (1993)
Glaser, B.G.: Basics of Grounded Theory Analysis. Sociology Press, Mill Valley (1992)
Glaser, B.G., Strauss, A.L.: The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine, Chicago (1967)
Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)
Hunt, S.D.: Marketing Theory: The Philosophy of Marketing Science. Irwin, Homewood, IL (1983)
Hunt, S.D.: Controversy in Marketing Theory: For Reason, Realism, Truth and Objectivity. M.E. Sharpe, Armonk (2003)
Hunt, S.D.: Truth in Marketing Theory and Research. Journal of Marketing 54, 1–15 (1990)
Lee, N., Lings, I.: Doing Business Research. Sage, London (2008)
Senior, C., Russell, T.: Cognitive neuroscience for the 21st century. Trends in Cognitive Science 4, 444–445 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lee, N., Greenley, G. (2010). Data Mining and Scientific Knowledge: Some Cautions for Scholarly Researchers. In: Casillas, J., Martínez-López, F.J. (eds) Marketing Intelligent Systems Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15606-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-15606-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15605-2
Online ISBN: 978-3-642-15606-9
eBook Packages: EngineeringEngineering (R0)