Abstract: As an ever increasing number of uses deliver gushing information, clustering data streams has turned into an essential strategy for information and learning designing. A typical approach is to abridge the data streams progressively with an online procedure into an extensive number of alleged smaller scale bunches (micro-clusters). Micro-clusters are representatives for set of similar data points and are created using a single pass over the data. A conventional clustering algorithm is used in a second offline step to re-cluster the micro-clusters into final clusters sometimes referred to as macro-clusters. This paper depicts Novel Selection, is applied to the medical datasets which has many attributes. In the online stage for the selected disease name in the dataset micro-clusters are formed whereas in offline stage the doctor name is chosen for the selected disease, so that the macro-clusters are formed. This is done by the concept of shared density between the clusters i.e, which are similar to selected attributes, so the large number of smaller clusters will be created. Graph is plotted for both the clusters and also for the accuracy. The clustering quality will be increased by using shared density concept.
Keywords: Data mining, data stream clustering, density-based clustering.