انجمن کامپیوتر ایران

In this paper, we proposed a new feature subset selection approach. In proposed approach first, the entire dataset are classified and the best number of clusters over it are found according to silhouette value. Then according to this value, each feature is alone classified with the same cluster number and accordingly the proposed entropy fuzzy measure is found for them. We examine our method on some traditional datasets. The results show a good performance of proposed method.

Uncertainty reasoning has been identified as an important and challenging issue in the database research. Many logic frameworks have been proposed to represent and reason about uncertainty in deductive databases. On the basis of the way in which uncertainties are associated with the facts and rules in programs, the approaches of these frameworks have been classified into “annotation based (AB)” and “implication based (IB).” When extending both frameworks with certainty constraints, they become equivalent in terms of expressive power. In this paper, we propose a uniform environment to evaluate and experiment with logic programs in AB and IB frameworks at the same time. We also extend the existing query processing to handle certainty constraints and we carry out experiments to evaluate its performance. Our experiments and results indicate that the proposed techniques yield tools that are capable to reason with uncertainty.