Clustering via C-spheres Agglomeration

The authors present a novel clustering approach called C-spheres Agglomeration (CA) that need not pre-specify the number of clusters; the input partition is autonomously determined by the input nature. The input data set first undergoes a preprocess and is partitioned into arbitrarily specified M_f subsets, each subset contains a portion of input items represented by a unique prototype c_j, j=1,2,.. M_f. Following the pre-process, the set {c_j, j=1,2,.. M_f} undergoes an iterative agglomeration process to extract clusters. Our idea to clustering is to treat each prototype c_j as a neutral particle in the feature space with mass m_j = number of input vectors represented by c _j. Also associated with each c_j is a C-sphere, the radius of which is defined in accordance with a local density measure around c_j. Prototypes are considered as neighbors if their C-spheres intersect. During one agglomeration iteration, every C-sphere is required to move directly to the corresponding centroid of neighborhoods