A new clustering algorithm for coordinate-free data
This paper presents the colored farthest-neighbor graph (CFNG), a new method for finding clusters of similar objects. The method is useful because it works for both objects with coordinates and for objects without coordinates. The only requirement is that the distance between any two objects be computable. In other words, the objects must belong to a metric space. The CFNG uses graph coloring to improve on an existing technique by Rovetta and Masulli. Just as with their technique, it uses recursive partitioning to build a hierarchy of clusters. In recursive partitioning, clusters are sometimes split prematurely, and one of the contributions of this paper is a way to reduce the occurrence of such premature splits, which also result when other partition methods are used to find clusters.
metric space graph coloring partition cluster analysis