Clustering Partial Lexicographic Preference Trees (Student Abstract)
Keywords:Preferences, Clustering, Decision Theory, Visual Analytics
AbstractIn this work, we consider distance-based clustering of partial lexicographic preference trees (PLP-trees), intuitive and compact graphical representations of user preferences over multi-valued attributes. To compute distances between PLP-trees, we propose a polynomial time algorithm that computes Kendall's Tau distance directly from the trees and show its efficacy compared to the brute-force algorithm. To this end, we implement several clustering methods (i.e., spectral clustering, affinity propagation, and agglomerative nesting) augmented by our distance algorithm, experiment with clustering of up to 10,000 PLP-trees, and show the effectiveness of the clustering methods and visualizations of their results.
How to Cite
Allen, J., Liu, X., Umapathy, K., & Reddivari, S. (2021). Clustering Partial Lexicographic Preference Trees (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15751-15752. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17872
AAAI Student Abstract and Poster Program