A Research and Application of Continuous Attributes Discretization Based on Cloud Model and Information Entropy
Discretization of continuous attributes is one of the most important issues in network data preprocessing. In this paper, a discretization algorithm of continuous attributes based on cloud model and information entropy is proposed. Making use of cloud transform, the proposed algorithm partitions the domain of every continuous attribute into many concepts represented by cloud models. The uncertain boundary of cloud model is more appropriate for actual data distribution. Define information entropy for every candidate cloud that treated as a measuring of importance. On the basis of that, select appropriate neighboring concepts and merge them. It could increase the information granularity of information system. Then utilize the fuzziness of cloud model for realizing the adaptive adjustment of boundary data, so as to improve consistency level in decision table. By employing the new algorithm, the experiments on Iris data set reached the expectation. The results show that the algorithm is feasible and efficient.