Abstract

As a result of emerging new techniques for scientific way of collecting data, we are able to accumulate data in large scale pertaining to various fields. One such method of data mining is Cluster analysis. Of all clustering algorithms, density-based clustering is better in terms of clustering quality and the way the data are handled. Density based clustering is advantageous over other clustering algorithms in the following ways – arbitrary shaped clusters are formed; number of clusters need not be known and noise is handled. However, there are two main points that are critical in density-based clustering. Firstly, it is not effective while handling datasets of varied density. Secondly, the selection of input parameters e and MinPts play a critical role in the quality of clustering. This paper proposes a model – Automatic Generation of Parameters in Density-Based Spatial Clustering (AGP-DBSCAN) that aims at improving the density-based clustering by generating different candidate parameters. With these candidates, we will be able to handle both uniform density and varied density datasets. The results of experiments also look promising for different clustering datasets.