Rough set based attribute reduction approach in data mining

In previous attribute reduction researches, the criteria of reduction are intended that the numbers of attributes are the least, the last rules are the simplest or amount of reduction is the most. But in database theory, the criteria are that the redundancy of attributes and dependency of attributes are as few as possible. According to these, authors propose the rough set based attribute reduction algorithm. The decision table is judged firstly whether or not it is consistent. To the complete consistent table, using the knowledge of rough set and information theory, authors get attribute reduction set by discernibility matrix, and compute relevance of attributes through conditional entropy. The best attribute reduction is the set which value is the minimum of average of attribute relevance. To the complete inconsistent table, authors make directly the decision rules with rough operator. The experiment shows it can get better effect. Reduction results of UCI databases are gotten through using the algorithm.