Clustering focuses on grouping similar objects in one cluster and dissimilar objects into another cluster. In clustering, this concept of boosting applies to the area of predictive data mining to generate multiple clusters. There is an existing cluster based boosting(CBB) system which focus on real data sets applied to it as input. It uses K-means algorithm that evolved in limited number of clusters with over fitting and it also holds two limitations: 1.Subsequent functions ignoring troublesome areas 2.Complex subsequent functions. To overcome these drawbacks hierarchical clustering is proposed and thus enhances the accuracy of desired output of CBB approach compared to popular boosting algorithm. The comparative analysis may show the improvement in performance of the system. The users may obtain refined clusters with more accuracy as desired output.

Boosting, Clustering, Hierarchical clustering, Classifier combining, Machine Learning, Supervised learning, Computer graphics, Artificial intelligence

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