Publication Type
Journal Article
Version
publishedVersion
Publication Date
10-2001
Abstract
Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization.
Keywords
Data mining, artificial intelligence, algorithms, decision trees
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Information Systems and Management
Publication
Industrial Management and Data Systems
Volume
101
Issue
1
First Page
41
Last Page
46
ISSN
0263-5577
Identifier
10.1108/02635570110365989
Publisher
Emerald
Citation
LEE, Sang Jun and SIAU, Keng.
A review of data mining techniques. (2001). Industrial Management and Data Systems. 101, (1), 41-46.
Available at: https://ink.library.smu.edu.sg/sis_research/9490
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1108/02635570110365989
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons