A density-based clustering algorithm, called OUTCLUST, is presented. The algorithm exploits a notion of local density in order to find homogeneous groups of objects as opposite to objects mostly deviating from the overall population. The proposed algorithm tries to simultaneously consider several features of real data sets, namely finding clusters of different shapes and densities in high dimensional data in presence of noise. It is shown that the method is able to identify very meaningful clusters, and experimental comparison with partitioning, hierarchial, and density-based clustering algorithms, is presented, pointing out that the algorithm achieves good clustering quality.
Subjects: 12. Machine Learning and Discovery