Healthcare Data Clustering with Summary Analysis under Multiple Databases (International Journal of Engineering and Computer Science, 2016)
Clustering methods are applied to group the relevant records. Partition based and hierarchy based clustering methods are adapted in the clustering process. Tree based data values and transaction data values are grouped using the clustering process. Transaction similarity is estimated using the distance measures. Data and their geometrical structures are used in the grouping process.            Peer-to-Peer network environment supports multiple database access under the distributed manner. Computational load and communication complexity parameters are considered in the distributed database building process. Distributed data partitioning operations are carried out using the General Decentralized Clustering (GDCluster) mechanism. Data values are formed as summarized views and applied in the clustering tasks. Partition and density based clustering operations are carried out on the summarized views. The GD clustering technique handles the dynamic data values. Weighted K Means clustering algorithm is adapted to perform the distributed data clustering process on healthcare data values.            The General Decentralized (GDCluster) clustering technique is enhanced to support partition and hierarchical data values. Summary analysis model is optimized to handle the hierarchical and grid based data items. The similarity estimation tasks are performed with the priority features. Data update period is also considered in the clustering process.  The performance analysis shows that Enhanced GD Clustering scheme reduces the communication delay with high accuracy levels.