IADIS Int. Conf. Intelligent Systems and Agents 2010,ISA, IADIS European Conference on Data Mining 2010,DM, Part of the MCCSIS 2010, Freiburg, Germany, 28 - 31 July 2010, pp.3-10
We consider the clustering problem with arbitrary shapes and different densities both within and between the clusters, where the number of clusters is unknown. We propose a new density-based approach in the graph theory context. The proposed algorithm has three phases. The first phase makes use of graph-based and density-based clustering approaches in order to identify the neighborhood structure of data points. The second phase detects outliers using the local outlier concept. In the third phase, a hiearchical agglomeration is performed to form the final clusters. The algorithm is tested on a number data sets and found to be effective. © 2010 IADIS.