DBSCAN ( Density Based Clustering Method with Connective Regions ) - A Survey

Data mining has suit an important in research area because of its ability to get valuable information from the data. The data mining uses various clustering algorithms for grouping related objects. One of the most important clustering algorithm is density based clustering algorithm, which groups the related objects in non linear shapes structure based on the density. But it has the problem of varied density, which does not find out meaningful clusters. To overcome this problem an improved NDCMD(A unified novel density based clustering using multidimensional spatial data) is used. In this paper, we also present PDBSCAN, a new density-based clustering algorithm based on DBSCAN for analysis of places and events using a collection of geo-tagged photos. We thereby introduce two new concepts: (1) density threshold, which is defined according to the number of people in the neighborhood, and (2) adaptive density, which is used for fast convergence towards high density regions.