A review of data mining techniques
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 41-46 |
| Journal / Publication | Industrial Management and Data Systems |
| Volume | 101 |
| Issue number | 1 |
| Publication status | Published - 2001 |
| Externally published | Yes |
Link(s)
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.
Research Area(s)
- Algorithms, Artificial intelligence, Data mining, Decision trees
Bibliographic Note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to lbscholars@cityu.edu.hk.
Citation Format(s)
A review of data mining techniques. / Lee, Sang Jun; Siau, Keng.
In: Industrial Management and Data Systems, Vol. 101, No. 1, 2001, p. 41-46.
In: Industrial Management and Data Systems, Vol. 101, No. 1, 2001, p. 41-46.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
