An NML-based model selection criterion for general relational data modeling
Yoshiki Sakai, Kenji Yamanishi · 2013
Whereas the main interest in most existing data mining approaches has been sequence data on a single type of object, namely attribute data, real-world databases store information about multiple relationships between various classes of objects. The modeling of these general relational data (GRD) plays an important role in eliciting knowledge across multiple relations. It is not reasonable to directly apply existing modeling methods to GRD, because GRD have statistical properties that distinguish them from attribute data. In this paper, we address the issue of statistical model selection in GRD modeling. From the viewpoint of the minimum description length principle, we propose a new model selection criterion by considering the statistical properties of GRD. We employ the normalized maximum likelihood code-length as a model selection criterion, and provide an asymptotic expansion theorem for its application to GRD modeling. To demonstrate its use in a critical application, we apply our proposed criterion to the issue of model selection in relational data clustering. An experiment using artificial datasets demonstrates the effectiveness of our technique compared to other criteria, and we also present a brand analysis using real beer-purchase data.