Incorporating Spatial Similarity into Ensemble Clustering

This paper addresses a fundamental problem in ensemble clustering – namely, how should one compare the similarity of two clusterings? The vast majority of prior techniques for comparing clusterings are entirely partitional, i.e., they examine assignments of points in set theoretic terms after they have been partitioned. In doing so, these methods ignore the spatial layout of the data, disregarding the fact that this information is responsible for generating the clusterings to begin with. In this paper, we demonstrate the importance of incorporating spatial information into forming ensemble clusterings. We investigate the use of a recently proposed measure, called CDistance, which uses both spatial and partitional information to compare clusterings. We demonstrate that CDistance can be applied in a wellmotivated way to four areas fundamental to existing ensemble techniques: the correspondence problem, subsampling, stability analysis and diversity detection.