Pushing Novelty Criterion Into Incremental Mining Algorithm

Classification is an important problem in data mining. Decision tree induction is one of the most common techniques that are applied to solve the classification problem. Many decision tree induction algorithms have been proposed based on different attribute selection and pruning strategies. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for decision tree induction. In this paper, the authors propose an incremental tree induction algorithm that integrates novelty criterion during tree induction. One of the main features of the proposed approach is to capture the user background knowledge, which is monotonically augmented.