A Clustering Technique using Density Difference

Finding clusters with widely differing sizes, shapes and densities in presence of noise and outliers is a challenging job. The DBSCAN algorithm is a versatile clustering algorithm that can find clusters with differing size and shape in databases containing noise and outliers. But it cannot find clusters with different densities. We extend the DBSCAN algorithm so that it can also detect clusters that differ in densities. While expanding a cluster local density is taken into consideration. Starting with a core object a cluster is extended by expanding only those density connected core objects whose neighbourhood sizes are within certain ranges as determined by their neighbours already existing in the cluster. Our algorithm detects clusters even if they are not separated by sparse regions. The computational complexity of the modified algorithm (O(n log n)) remains same as the original DBSCAN