Comparing Clusterings in Space

This paper proposes a new method for comparing clusterings both partitionally and geometrically. Our approach is motivated by the following observation: the vast majority of previous 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. We demonstrate that this leads to a variety of failure modes. Previous comparison techniques often fail to differentiate between significant changes made in data being clustered. We formulate a new measure for comparing clusterings that combines spatial and partitional information into a single measure using optimization theory. Doing so eliminates pathological conditions in previous approaches. It also simultaneously removes common limitations, such as that each clustering must ...

• Type: Journal
• Year: 2010
• Where: ICML
• Authors: Michael H. Coen, M. Hidayath Ansari, Nathanael Fillmore
• Keywords: ICML 2010, Machine Learning, Spatial Layout, Theoretic Terms, Vast Majority