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- Type of Document: Article
- DOI: 10.1109/AICCSA.2007.370913
- Publisher: 2007
- Abstract:
- In this paper the Karnaugh and Quine-McCluskey methods are used for symbolic classification problem, and then these methods are compared with other famous available methods. Because the data in classification problem is very large, some changes should be applied in the original Quine-McCluskey (QMC) algorithm. We proposed a new algorithm that applies the QMC algorithm greedily calling it GQMC. It is surprising that GQMC results are most of the time equal to QMC. GQMC is still very slow classifier and it can be used when the number of attributes of the data is small, and the ratio of training data to the all possible data is high. © 2007 IEEE
- Keywords:
- Quine-McCluskey (QMC) algorithm ; Algorithms ; Data reduction ; Problem solving ; Classification (of information)
- Source: 2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007, Amman, 13 May 2007 through 16 May 2007 ; 2007 , Pages 404-411 ; 1424410312 (ISBN); 9781424410316 (ISBN)
- URL: https://ieeexplore.ieee.org/abstract/document/4230988