Clustering Partial Lexicographic Preference Trees (Student Abstract)

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 PLPtrees, we propose a polynomial time algorithm that computes Kendall’s τ 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.

Authors

• Joseph Allen
• Xudong Liu
• Karthikeyan Umapathy
• Sandeep Reddivari

Keywords

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