Combining clustering of preference rankings with unfolding and vector models

As a multitude of real data problems involve preference ranking data, in the last decades analysis of rankings has become increasingly important and interesting in several fields such as behavioral sciences, machine learning and decision making. A combination of cluster analysis of preference ranking data with vector models and unfolding technique is presented. Vector models and unfolding techniques are used to analyze preference ranking data and are based on badness of fit functions. Both methods map the preferences in a joint low-dimensional space. For the clustering part the K-Medians Cluster Component Analysis method is used. Real data sets are analyzed to illustrate the proposed approach.