Multilayer clustering: A discovery experiment on country level trading data

Abstract

The topic of this work is the presentation of a novel clustering methodology based on instance similarity in two or more attribute layers. The work is motivated by multi-view clustering and redescription mining algorithms. In our approach we do not construct descriptions of subsets of instances and we do not use conditional independence assumption of different views. We do bottom up merging of clusters only if it enables reduction of an example variability score for all layers. The score is defined as a two component sum of squared deviates of example similarity values. For a given set of instances, the similarity values are computed by execution of an artificially constructed supervised classification problem. As a final result we identify a small but coherent clusters. The methodology is illustrated on a real life discovery task aimed at identification of relevant subgroups of countries with similar trading characteristics in respect of the type of commodities they export.