Abstract
Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classification of the available data mining techniques is provided, and a comparative study of such techniques is presented.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 866-883 |
| Number of pages | 18 |
| Journal | IEEE Transactions on Knowledge and Data Engineering |
| Volume | 8 |
| Issue number | 6 |
| DOIs | |
| State | Published - 1996 |
| Externally published | Yes |
Keywords
- Association rules
- Classification
- Data clustering
- Data cubes
- Data generalization and characterization
- Data mining
- Knowledge discovery
- Multiple-dimensional databases
- Pattern matching algorithms
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics
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