The basic concepts and the relating theorem were studied in this paper.Knowledge reduction is one of the most important contents of rough set theory.The attribute reduction algorithm based on the frequency of attributes used the degree of the attributes appear in the discernibility matrix as the heuristic information of attributes selection.According to the limitation of this algorithm,an improved algorithm was presented.Finally the validity of the improved algorithm was analyzed and demonstrated by the example.

Electric load forecasting is an important and challengeable work. In order to accurately forecast the loads of power system,this article presents a new short-term load forecasting method based on optimized decision tree,which efficiently takes the non-load factors’ influences into account. After preprocessing the sample data,rough set is used to reduce the testing attributes of decision tree. Good test results using actual data demonstrate that this method could improve the accuracy of short-term load forecasting ef-fectively,and it has practicability and superiority.

The attribute reduction and relative attribute reduction were discussed in this paper. They are the core of KDD. The information view and the algebra view of rough set theory were combined and a novel attribute reduction algorithm was proposed. In the algorithm, the core attribute set which is the initial candidate reduction set is obtained from the discernibility matrix. The frequency of attributes, got from the filtered discernibility matrix, is used as the heuristic information of attributes selection. The algorithmpsilas terminal condition is realized by the conditional entropy. Taking the climatic factor reduction in load forecasting as an example, it has proved that the algorithm requires less computation, has high efficiency and can reduce the redundant attribute in the relative reduction set to a certain extent.

Rough set theory is an effective approach to imprecision, vagueness and incompleteness in classification analysis and knowledge discovery. Attribute reduction and relative attribute reduction are the core of KDD. From the point of view of information, the basic concepts of rough set were analyzed in this paper. A novel attribute reduction algorithm was constructed by adopting conditional entropy and the improved importance of attribute. This algorithm does not calculate the attribute core but directly reduces the original attribute set. The performance of this algorithm was compared with that of the old algorithm based on mutual information by using some classical databases in the UCI repository. Finally, the validity and the feasibility of the algorithm are demonstrated by the experiment results.

In this paper, a novel and effective algorithm is introdcued for constructing decision tree. First of all, the knowledge dependence in rough set theory is used to reduce the test attribute set of decision tree, that is, the test attribute space is optimized and hence the attributes which are not correlated with the decision information are deleted. Then in view of the shortcomings existing in ID3 algorithm, the degree of dependency of decision attribute on condition attribute is used as a heuristic information for selecting the attribute that will best sepatate the samples into individual classes. Thus the repetition of the decision subtrees and some attributes to be chosen many times on the same decision tree are resolved. The example shows that the method is better than the ID3 algorithm and has been verified to be effective.