A local density based spatial clustering algorithm with noise

Density-based clustering algorithms are attractive for the task of class identification in spatial database. However, in many cases, very different local-density clusters exist in different regions of data space, therefore, DBSCAN[I] using a global density parameter is not suitable. As an improvement, OPTICS[2] creates an augmented ordering of the database representing its density-based clustering structure, but it only generates the clusters whose local-density exceeds some threshold instead of similar local-density clusters and doesn't produce a clustering of a data set explicitly. Furthermore the parameters required by almost all the well-known clustering algorithms are hard to determine but have a significant influence on the clustering result. In this paper, a new clustering algorithm LDBSCAN relying on a local-density-based notion of clusters is proposed to solve those problems and, what is more, it is very easy for us to pick the appropriate parameters and takes the advantage of the LOF[3] to detect the noises comparing with other density-based clustering algorithms. The proposed algorithm has potential applications in business intelligence and enterprise information systems.