• K Shyam Sunder Reddy

Data stream clustering is a significant method for handling huge quantity of dynamic data. In recent days data streams are generated through numerous applications which increases the complexity of handling huge data. To reduce such kind of overhead several techniques and algorithms are proposed. This paper proposes an algorithm called Complete Binary Tree Mapping Micro Clusters (CBTMMC) which converts the complete dataset into the complete binary tree, in which the left sub tree contains high density data points and the right sub tree contains low density data points. The maximum of the outliers are obtained by right sub tree leaf node. So clustering and detecting outlier through proposed approach is simple in less time and space consuming. The experiment results shows that the proposed system provides high performance than the existing systems on the real-time datasets.

Data stream, Density-based clustering, Micro-clustering, Complete Binary Tree.

"DATA STREAM CLUSTERING BASED ON A COMPLETE BINARY TREE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.522-528, June 2019, Available :http://www.jetir.org/papers/JETIR1908544.pdf

"DATA STREAM CLUSTERING BASED ON A COMPLETE BINARY TREE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp522-528, June 2019, Available at : http://www.jetir.org/papers/JETIR1908544.pdf