<P> If a point is found to be a dense part of a cluster, its ε - neighborhood is also part of that cluster . Hence, all points that are found within the ε - neighborhood are added, as is their own ε - neighborhood when they are also dense . This process continues until the density - connected cluster is completely found . Then, a new unvisited point is retrieved and processed, leading to the discovery of a further cluster or noise . </P> <P> DBSCAN can be used with any distance function (as well as similarity functions or other predicates). The distance function (dist) can therefore be seen as an additional parameter . </P> <P> The algorithm can be expressed in pseudocode as follows: </P> <P> where RangeQuery can be implemented using a database index for better performance, or using a slow linear scan: </P>

The dbscan algorithm results in every point in a data set belonging to a cluster