Description
Clustering of data streams finds important applications in tracking evolution of various phenomena in medical, meteorological, astrophysical, seismic studies. Algorithms designed for this purpose are capable of adapting the discovered clustering model to the changes in data characteristics but are not capable of adapting to the user’s requirements themselves. Based on the previous observation, we perform a comparative study of different approaches for existing stream clustering algorithms and present a parameterized architectural framework that exploits nuances of the algorithms. This framework permits the end user to tailor a method to suit his specific application needs. We give a parameterized framework that empowers the end-users of KDD technology to build a clustering model. The framework delivers results as per the user’s application requirements. We also present two assembled algorithms G-kMeans and G-dbscan to instantiate the proposed framework and compare the performance with the existing stream clustering algorithms.