CUBN: A clustering algorithm based on density and distance
Li Wang, Wang Zheng-ou · 2004
In data mining, clustering is used to discover groups and identify interesting distribution on the underlying data. Traditional clustering algorithm favors clusters with spherical shapes and similar sizes. We propose a new clustering algorithm called CUBN that integrates density-based and distance-based clustering. Firstly CUBN finds border points by using erosion operation that is one of the basic operations in mathematical morphology, then, it clusters the border points and inner points according to the nearest distance. Our experimental results show that CUBN can identify clusters having non-spherical shapes and wide variances in size, and its computational complexity is O(n). Therefore, this algorithm facilitates the clustering of a very large data set.