**6.4 Overview of DBSCAN algorithm as a feature exactor**

DBSCAN and clustering algorithm is used for feature Exactor which works on the densities (International Workshop on Text- Based information Retrieval (TIR 05),University of Koblenz-Landau, Germany). It separates the set D into subsets of similar densities. In the best case they can find out the cluster number k routinely and categorize the clusters of random shape and size.The runtime of this algorithms is in magnitude of O(n log(n)) for low-dimensional data (Busch,2005). A density-based cluster algorithm is based on two properties given below (TIR 05,University of Koblenz-Landau, Germany).


In DBSCAN a region is defined as the set of points that lie in the -neighborhood of some point p. if |C| exceeds a given Min Points-threshold Cluster label propagates from p to the other points in C. The complete description of DBSCAN algorithm is provided in (Ester et al., 1996;Tan et al., 2004;Dagher. I et al., 1999).
