AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Comprehensive Cross-Hierarchy Cluster Agreement Evaluation
David M. Johnson, Caiming Xiong, Jing Gao, Jason J. Corso

Last modified: 2013-06-29

Abstract


Hierarchical clustering represents a family of widely used clustering approaches that can organize objects into a hierarchy based on the similarity in objects' feature values. One significant obstacle facing hierarchical clustering research today is the lack of general and robust evaluation methods. Existing works rely on a range of evaluation techniques including both internal (no ground-truth is considered in evaluation) and external measures (results are compared to ground-truth semantic structure). The existing internal techniques may have strong hierarchical validity, but the available external measures were not developed specifically for hierarchies. This lack of specificity prevents them from comparing hierarchy structures in a holistic, principled way. To address this problem, we propose the Hierarchy Agreement Index, a novel hierarchy comparison algorithm that holistically compares two hierarchical clustering results (e.g. ground-truth and automatically learned hierarchies) and rates their structural agreement on a 0-1 scale. We compare the proposed evaluation method with a baseline approach (based on computing F-Score results between individual nodes in the two hierarchies) on a set of unsupervised and semi-supervised hierarchical clustering results, and observe that the proposed Hierarchy Agreement Index provides more intuitive and reasonable evaluation of the learned hierarchies.

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