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Finding Accurate And Comprehensible Knowledge Discovery In Database Model | ||
| Journal of University of Babylon | ||
| Article 1, Volume 21, Issue 3, 2013, Pages 725-745 | ||
| Author | ||
| Abbas M. AL-Bakry | ||
| Abstract | ||
| Available data in scientific fields mainly consist of huge datasets which gathered by a different techniques. These data are saved in much diversified and often incompatible repositories, such of these data are in bioinformatics, geoinformatics, astroinformatics and Scientific World Wide Web sites. From the other hand, the lack of reference data is very often to give a poor performance of learning. One of the key problems in supervised learning is due to the insufficient size of the trained dataset. Therefore, we suggest developing a theoretical and practical valid tool for analyzing small of the data sample which remains as a critical and challenging issue for the researches. In this work we suggest to design and develop instructions and tools for knowledge discovery from any type of database domain, based on the state of the art information. The proposed method to construction data, determining the best features for each dataset, generating association rules then classifying and simplifying these rules. | ||
| Keywords | ||
| Knowledge Discovery; Data sets; GPDCM; Random Forest | ||
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