Survey on Approaches, Problems and applications of the Boosting

Abstract 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 iterative process to increase the accuracy of the supervised learning algorithm i.e. classifiers. Clustering with boosting improves quality of mining process. In boosting, misclassified instances by initial classifier are used for learning of subsequent classifiers and set of classifiers is used to classifying further instances. Usage of boosting in many applications proved its effectiveness. Although its success, boosting had certain problems. It could not handle noisy data and data with troublesome areas. This limitation of boosting is solved by cluster based boosting in which data is clustered before boosting and depend on the cluster boosting is performed. CBB works well on benchmark data. Real world data contains many irrelevant features. In CBB all features data is used for clustering. Due to consideration of irrelevant feature there is possibility of inaccurate clustering. Inaccurate clustered data may result into negative effect on boosting performance. To overcome this issue feature selection will be applied before clustering on training data.