As a data analysis technology, data mining has matured to the extent that there are now a number of sophisticated commercial software packages available. The purpose of this article is to explore what data mining has become, its relationship to statistics and its relevance in market research.
Get full access to this article
View all access options for this article.
References
1.
AdamsN.M., HandD.J. & TillR.J. (2001) Mining for classes and patterns in behavioural data.Journal of the Operational Research Society, 52, 9, pp. 1017–1024.
2.
AgrawalR., ImielinskiT. & SwamiA. (1993) Mining association rules between sets of items in large databases.Proceedings of the ACM SIGMOD Conference on Management of Data (SIGMOD ‘93), ACM Press, pp. 307–328.
3.
ChiuS. & TavellaD. (2008) Data Mining and Market Intelligence for Optimal Marketing Returns, Burlington, MA: Butterworth-Heinemann.
4.
GoldbergD., NicholsD., OldB.M. & TerryD. (1992) Using collaborative filtering to weave an information tapestry.Communications of the ACM, 35, 12, pp. 61–70.
5.
HandD.J. (1999) Statistics and data mining: intersecting disciplines.SIGKDD Explorations, 1, pp. 16–19.
6.
HandD.J., AdamsN.M. & HeardN.A. (2005) Pattern discovery tools for detecting cheating in student coursework. In MorikK., BoulicaultJ.-F. & SiebesA. (eds) Local Pattern Detection. Lecture notes in Computer Science 3539 (Springer), pp. 39–50.
7.
HandD.J., MannilaH. & SmythP. (2001) Principles of Data Mining.Cambridge, MA: MIT Press.
8.
HandD.J., KellyM.J., BluntG. & AdamsN.M. (2000) Data mining for fun and profit.Statistical Science, 15, 2, pp. 111–126.
9.
HastieT., TibshiraniR. & FriedmanJ. (2001) The Elements of Statistical Learning.New York: Springer.
10.
MaysE. (2005) Handbook of Credit Scoring.Chicago, IL: Financial World Press.
11.
R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (http://www.R-project.org).
12.
ShearerC. (2000) The CRISP-DM model: the new blueprint for data mining.Journal of Data Warehousing, 5, 4, pp. 13–22.
13.
WassermanL. (2004) All of Statistics: A Concise Course in Statistical Inference.New York: Springer.
14.
WestonD.J., HandD.J., AdamsN.M., WhitrowC. & JuszczakP. (2008) Plastic card fraud detection using peer group analysis.Advances in Data Analysis and Classification, 2, 1, pp. 45–62.
15.
WittenI.H. & FrankE. (2005) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (2nd edn). San Francisco, CA: Morgan Kaufmann.