CMUNE: A clustering using mutual nearest neighbors algorithm
Mohamed Abbas, Amin Shoukry · 2012
A novel clustering algorithm CMune is presented for the purpose of finding clusters of arbitrary shapes, sizes and densities in high dimensional feature spaces. It can be considered as a variation of the Shared Nearest Neighbor algorithm (SNN), in which each sample data point votes for the points in its k-nearest neighborhood. Sets of points sharing a common mutual nearest neighbor are considered as dense regions/blocks. These blocks are the seeds from which clusters may grow up. Therefore, CMune is not a point-to-point clustering algorithm. Rather, it is a block-to-block clustering technique. Much of its advantages come from this fact: Noise points and outliers correspond to blocks of small sizes, and homogeneous blocks highly overlap. The algorithm has been applied to a variety of low and high dimensional data sets with superior results over existing techniques such as K-means, DBScan, Mitosis and Spectral clustering. The quality of its results as well as its time complexity, place it at the front of these techniques.