Improved dbscan clustering algorithm using SR-tree

The tremendous amount of data produced now a days in various application domains such as molecular biology or geography can only be fully exploited by efficient and effective data mining tools. One of the primary data mining tasks is clustering, which is the task of partitioning points of a data set into distinct groups (clusters) such that two points from one cluster are similar to each other whereas two points from distinct clusters are not. The detection of clusters in a given dataset is important for data analysis. This paper presents a possible DBSCAN clustering algorithm implementation. DBSCAN algorithm is based on density reachable and density connected point. Adding distributed SR-tree technique to density clustering algorithm we can improve clustering results.