A robust clustering algorithm based on competitive agglomeration and soft rejection of outliers

We present a new clustering algorithm that addresses two major issues associated with conventional partitional clustering: the difficulty in determining the number of clusters, and the sensitivity to noise and outliers. The proposed algorithm determines the number of clusters by a process of competitive agglomeration. Noise immunity is achieved by integrating concepts from robust statistics into the algorithm. The proposed approach can incorporate different distance measures in the objective function to find an unknown number of clusters of various types including lines, planes and surfaces