Supervised Clustering of Label Ranking Data

In this paper we study supervised clustering in the context of label ranking data. Segmentation of such complex data has many potential real-world applications. For example, in target marketing, the goal is to cluster customers in the feature space by taking into consideration the assigned, potentially incomplete product preferences, such that the preferences of instances within a cluster are more similar than the preferences of customers in the other clusters. We establish several heuristic baselines for this application that make use of well-known algorithms such as K-means, and propose a principled algorithm specifically tailored for this type of clustering. It is based on the PlackettLuce (PL) probabilistic ranking model. Each cluster is represented as a union of Voronoi cells defined by a set of prototypes and is assigned a set of PL label scores that determine the cluster-specific label ranking. The unknown cluster PL parameters and prototype positions are determined using a supervised learning technique. Cluster membership and ranking for a new instance is determined by membership of its nearest prototype. The proposed algorithms were empirically evaluated on synthetic and reallife label ranking data. The PL-based method was superior to the heuristically-based supervised clustering approaches. The proposed PL-based algorithm was also evaluated on the task of label ranking prediction. The results showed that it is highly competitive to the state of the art label ranking algorithms, and that it is particularly accurate on data with