CNclustering: Clustering with compatible nucleoids

Dissimilarity measure plays a very important role in traditional data clustering. In this paper, we extend the dissimilarity measure as compatible measure and present a new algorithm (CNclustering) based on this measure. The algorithm is a rigorous partition method, it first gets some compatible clusters with a Compclustering method as the initial nucleoids, then absorbs other objects by the absorbing step to form the final clusters. We use S20 and S200 data sets to demonstrate the clustering performance of the algorithm and get some consistent results.