Clustering Partial Lexicographic Preference Trees (Student Abstract)


  • Joseph Allen University of North Florida
  • Xudong Liu University of North Florida
  • Karthikeyan Umapathy University of North Florida
  • Sandeep Reddivari University of North Florida


Preferences, Clustering, Decision Theory, Visual Analytics


In 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



AAAI Student Abstract and Poster Program