Clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, web analysis, bioinformatics, and many others DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. The key idea of the DBSCAN algorithm is that for each data point in a cluster, the neighborhood within a given radius has to contain at least a minimum number of points. We have proposed improved evenhanded workload allocation using hierarchical (Tree-Based) approach for constructing of data cluster