| publication name | An inductive learning algorithm for discovering comprehensible knowledge from databases |
|---|---|
| Authors | Raafat A. El-Kammar, Atta E. El-Alfy, Mohamed I. Sharawy, Mohye- E. El-Alame |
| year | 2002 |
| keywords | |
| journal | Cairo University, Faculty of Computers and Information, The Egyptian Information Journal, 2002, Cairo, Egypt |
| volume | Not Available |
| issue | Not Available |
| pages | Not Available |
| publisher | Not Available |
| Local/International | International |
| Paper Link | Not Available |
| Full paper | download |
| Supplementary materials | Not Available |
Abstract
Most of the existing rule induction algorithms are computationally cumbersome and consume much time specially on large noisy databases. This paper presents an efficient algorithm for extracting accurate and comprehensible set of rules from database. The algorithm transfers the attribute of continuous values into linguistic terms using suitable membership function (fuzzification). This transformation leads to the reduction of search space. The algorithm controls the rules induction through three levels. These levels are the search level (level1), the confidence level (pc), and the support level (DBC)