A new decision tree construction using the cloud transform and rough sets

Many present methods for dealing with the continuous data and missing values in information systems for constructing decision tree do not perform well in practical applications. In this paper, a new algorithm, Decision Tree Construction based on the Cloud Transform and Rough Set Theory under Characteristic Relation (DTCCRSCR), is proposed for mining classification knowledge from the data set. The cloud transform is applied to discretize continuous data and the attribute whose weighted mean roughness under the characteristic relation is the smallest will be selected as the current splitting node. Experimental results show the decision trees constructed by DTCCRSCR tend to have a simpler structure, much higher classification accuracy and more understandable rules than C5.0 in most cases.