An improved DBSCAN, a density based clustering algorithm with parameter selection for high dimensional data sets

Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Cluster analysis is one of the major data analysis methods. It is the art of detecting group of similar objects in large data sets without having specified groups by means of explicit features. The problem of detecting clusters is challenging when the clusters are of different size, density and shape. This paper gives a new approach towards density based clustering approach. DBSCAN which is considered a pioneer of density based clustering technique, this paper gives a new move towards detecting clusters that exists within a cluster. Based on various parameters needed for a good clustering the algorithm is evaluated such as number of clusters formed, noise ratio on distance change, time elapsed to form cluster, unclustered instances as well as incorrectly clustered instances.