Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-24T18:00:20.825Z Has data issue: false hasContentIssue false
  • English
  • Français

Data mining: past, present and future

Published online by Cambridge University Press:  07 February 2011

Frans Coenen
Frans Coenen*
Affiliation:
Department of Computer Science, The University of Liverpool, Liverpool L693BX, UK; e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Data mining has become a well-established discipline within the domain of artificial intelligence (AI) and knowledge engineering (KE). It has its roots in machine learning and statistics, but encompasses other areas of computer science. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale data mining to be conducted. Unlike other innovations in AI and KE, data mining can be argued to be an application rather then a technology and thus can be expected to remain topical for the foreseeable future. This paper presents a brief review of the history of data mining, up to the present day, and some insights into future directions.

Type
Articles
Information
The Knowledge Engineering Review , Volume 26 , Special Issue 1: 25th Anniversary Issue , 07 February 2011 , pp. 25 - 29
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agrawal, R., Imielinski, T., Swami, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'93), ACM Press, 207–216.Google Scholar
Breiman, L., Friedman, Y., Olshen, R., Stone, C. 1984. Classification and Regression Trees. Wadsworth.Google Scholar
Fayyad, U., Piatetsky-Shapiro, H., Smyth, P. 1996. The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39(11), 27–34.CrossRefGoogle Scholar
Han, J., Pei, J., Yin, Y. 2000. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD Conference on Management of Data (SIGMOD ’00), ACM Press, 1–12.Google Scholar
Hand, D. J., Yu, K. 2001. Idiot's Bayes: not so stupid after all? International Statistical Review 69, 385–398.Google Scholar
Hastie, T., Tibshirani, R. 1996. Discriminant adaptive nearest neighbor classification. IEEE Transaction on Pattern Analysis and Machibe Intelligence 18(6), 607–616.CrossRefGoogle Scholar
Liu, B., Hsu, W., Ma, Y. M. 1998. Integrating classification and association rule mining. In Proceedings of the Knowledge Discovery and Data Mining-98, ACM Press, 80–86.Google Scholar
MacQueen, J. B. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium Mathematical Statistics and Probability. University of California Press, Berkeley, CA, USA, 281–297.Google Scholar
Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc.Google Scholar
Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag.Google Scholar
Yan, X., Han, J. 2002. gSpan: graph-based substructure pattern mining. In Proceedings of the IEEE International Conference on Data Mining (ICDM '02), IEEE, 721–724.Google Scholar
Zhang, T., Ramakrishnan, R., Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, ACM Press, 103–114.Google Scholar