Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Boosting is the iterative process which aims to improve the predictive accuracy of the learning algorithms. Clustering with boosting improves quality of mining process. When supervised algorithms applied on training data for learning, there may be possibility of biased learning which affects the accuracy of prediction result. Boosting provides the solution for this. It generates subsequent classifiers by learning incorrect predicted examples by previous classifier. Boosting process possesses some limitations. Different approaches introduced to overcome the problems in boosting such as overfitting and troublesome area problem to improve performance and quality of the result. Cluster based boosting address limitations in boosting for supervised learning systems. In literature Cluster based boosting [6] is used to address limitations in boosting for supervised learning systems. In paper [6], k-means is used as a clustering algorithm. Encapsulation of another clustering method with CBB may result into increase in the performance. In our proposed work we used fuzzy c means (FCM), Expectation Minimization and Hierarchical algorithm with CBB and compared the results.

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Clustering Classification Boosting decision Tree