Enriched spatial comparison of clusterings through discovery of deviating subspaces

摘要

Generation and analysis of multiple clusterings is a growing and important research field. A fundamental challenge underpinning this area is how to develop principled methods for assessing and explaining the similarity between two clusterings. A range of clustering similarity indices exist and an important subclass consists of measures for assessing spatial clustering similarity. These provide the advantage of being able to take into account properties of the feature space when assessing the similarity of clusterings. However, the output of spatially aware clustering comparison is limited to a single similarity value, which lacks detail for a user. Instead, a user may also wish to understand the degree to which the assessment of clustering similarity is dependent on the choice of feature space.