Skip to main content
    • Aa
    • Aa
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 7
  • Cited by
    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    De Renzis, Alan Garriga, Martin Flores, Andres Cechich, Alejandra and Zunino, Alejandro 2016. Case-based Reasoning for Web Service Discovery and Selection. Electronic Notes in Theoretical Computer Science, Vol. 321, p. 89.

    Lahneman, William J. 2016. IC Data Mining in the Post-Snowden Era. International Journal of Intelligence and CounterIntelligence, Vol. 29, Issue. 4, p. 700.

    Ding, Fei and Zhuang, Yi 2015. Computing contingency tables from sparse ADtrees. Applied Intelligence, Vol. 42, Issue. 4, p. 777.

    Selene Xia, Belle and Gong, Peng 2014. Review of business intelligence through data analysis. Benchmarking: An International Journal, Vol. 21, Issue. 2, p. 300.

    Wang, Yuping Borlak, Jurgen and Tong, Weida 2014. Genomic Biomarkers for Pharmaceutical Development.

    Hallinan, J.S. 2012. Systems Biology of Bacteria.

    McBurney, Peter Parsons, Simon and Viroli, Mirko 2011. A quarter-century of The Knowledge Engineering Review: Introduction to the Special Issue. The Knowledge Engineering Review, Vol. 26, Issue. 01, p. 1.


Data mining: past, present and future

  • Frans Coenen (a1)
  • DOI:
  • Published online: 07 February 2011

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.

Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

R. Agrawal , T. Imielinski , A. Swami 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, 207216.

U. Fayyad , H. Piatetsky-Shapiro , P. Smyth 1996. The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39(11), 2734.

J. Han , J. Pei , Y. Yin 2000. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD Conference on Management of Data (SIGMOD ’00), ACM Press, 112.

D. J. Hand , K. Yu 2001. Idiot's Bayes: not so stupid after all? International Statistical Review 69, 385398.

T. Hastie , R. Tibshirani 1996. Discriminant adaptive nearest neighbor classification. IEEE Transaction on Pattern Analysis and Machibe Intelligence 18(6), 607616.

V. N. Vapnik 1995. The Nature of Statistical Learning Theory. Springer-Verlag.

T. Zhang , R. Ramakrishnan , M. Livny 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, 103114.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

The Knowledge Engineering Review
  • ISSN: 0269-8889
  • EISSN: 1469-8005
  • URL: /core/journals/knowledge-engineering-review
Please enter your name
Please enter a valid email address
Who would you like to send this to? *