On the generation of decision tree based on rough set model, for the sake of classification accuracy, existing algorithms usually partition examples too specific. And it is hard to avoid the negative impact caused by few special examples on decision tree. In order to obtain this priority in traditional decision tree algorithm based on rough set, the sample is partitioned much more meticulously. Inevitably, a few exceptional samples have negative effect on decision tree. And this leads that the generated decision tree seems too large to be understood. It also reduces the ability in classifying and predicting the coming data. To settle these problems, the restrained factor is introduced in this paper. For expanding nodes in generating decision tree algorithm, besides traditional terminating condition, an additional terminating condition is involved when the restrained factor of sample is higher than a given threshold, then the node will not be expanded any more. Thus, the problem of much more meticulous partition is avoided. Furthermore, the size of decision tree generated with restrained factor involved will not seem too large to be understood.
A novel approach to dynamic profiling of e-customers considering click stream data and online reviews
