Data mining: past, present and future
- 7 February 2011
- journal article
- review article
- Published by Cambridge University Press (CUP) in The Knowledge Engineering Review
- Vol. 26 (1), 25-29
- https://doi.org/10.1017/s0269888910000378
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.Keywords
This publication has 8 references indexed in Scilit:
- SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivationNature Genetics, 2008
- Idiot's Bayes—Not So Stupid After All?International Statistical Review, 2001
- Mining frequent patterns without candidate generationPublished by Association for Computing Machinery (ACM) ,2000
- The KDD process for extracting useful knowledge from volumes of dataCommunications of the ACM, 1996
- Discriminant adaptive nearest neighbor classificationIEEE Transactions on Pattern Analysis and Machine Intelligence, 1996
- BIRCHACM SIGMOD Record, 1996
- The Nature of Statistical Learning TheoryPublished by Springer Science and Business Media LLC ,1995
- Mining association rules between sets of items in large databasesPublished by Association for Computing Machinery (ACM) ,1993