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@article{145967,
        author = {Allu Pavankumar and Dr venkataramana},
        title = {Clustering Data stream Based on common thickness Between Micro-Clusters},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {849-851},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145967},
        abstract = {As more and more applications deliver streaming data, clustering data streams has become an crucialmethod for data and knowledge engineering. A normal approach is to summarize the data stream in real-time with an online process into countless called micro-clusters. Micro-clusters represent local density estimates by assemble the information of many data points in a defined area. On request, a (modified) traditional clustering algorithm is used in a second offline step to recluster the microclusters into larger final clusters. For reclustering, the coordinator of the micro-clusters is used as pseudo points with the density estimates used as their weights. However, information about density in the area between micro-clusters is not preserved in the online process and reclustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-clusters (e.g., uniform or Gaussian). This paper depicts DBSTREAM, the first micro-cluster-based online clustering component that explicitly captures the density between micro-clusters via a shared density graph. The density information in this graph is then exploited for reclustering based on actual density between modified micro-clusters. We discuss the space and time complexity of maintaining the shared density graph. Experiments on a wide range of artificial and real data sets highlight that using shared density improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller microclusters to achieve comparable results.},
        keywords = {Data mining, data stream clustering, density-based clustering.},
        month = {},
        }