Novel approach for data stream clustering through micro-clusters shared Density - Desai, Prashant V; Gaikawad, Vilas S (International Journal of Computer Sciences and Engineering, 2025)
Clustering is the process of organizing objects into groups whose members are similar in some way and is very important technique in data mining as it has its applications spread extensively, e.g. marketing, biology, pattern recognition etc. So summarize the data stream in the real life with the online process is called as micro-cluster but it shows the density when we are combining the data in the one place. In the offline process we are using the modification clustering algorithm to re-clustering into larger cluster. For that the center of micro-cluster point as the pseudo point with density randomly calculates their weight. That density information area of micro-cluster is not preserved the online process. So used DBSTREAM, the first micro-cluster based on online clustering component capture the density between micro-cluster via shared density graph. We develop and evaluate a new method to address this problem for micro-cluster-based algorithms. The density information in this graph is then exploited for re-clustering based on actual density between adjacent micro-clusters. For that shared density graph improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller micro-clusters to achieve comparable results.