In Data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is a one of the problem arises due to data source which provide unequal class i.e. examples of one class in a training data set vastly outnumber examples of the other class(es). Researchers have rigorously studied several techniques to alleviate the problem of class imbalance, including resampling algorithms, and feature selection approaches to this problem. In this paper, we present a new hybrid frame work dubbed as Cluster Disjunct Minority Oversampling Technique (CDMOTE) and Naïve Bayes for Cluster Disjunct (NBCD) for learning from skewed training data. These algorithms provide a simpler and faster alternative by using cluster disjunct concept. We conduct experiments using fifteen UCI data sets from various application domains using four algorithms for comparison on six evaluation metrics. The empirical study suggests that CDMOTE and NBCD have been believed to be effective in addressing the class imbalance problem.