Boundary Detecting Algorithm for Each Cluster based on DBSCAN

Detecting Detecting the boundary of each cluster in a data set is a tough problem for many existed boundary detecting algorithms. In order to solve that problem, a clustering boundary detecting algorithm based on DBSCAN named BDAEC(Boundary Detecting Algorithm for Each Cluster based on DBSCAN: BDAEC) is proposed. Firstly, according to the core point percent and the density value of each data object, all the core points are extracted by this algorithm from the data set. Then, many connected undirected graphs will be constituted by these core points. And the cluster numbers of the data set can be known by those connected undirected graphs for each one of them represents a cluster. Finally, Eps field will be diveded into two fields: the positive field and the negative field. And the boundary of each cluster or the whole data set can be detected by the distribution characteristics of the data objects which are located in the positive field and negative field of the given data object. The experimental results on many data sets with noise show that BDAEC algorithm can obtain the numbers and the boundaries of the clusters with different size or shapes effectively.