Upgrading Bayesian Clustering to First Order Logic
Jan Ramon, Luc De Raedt · Lirias · 1999
Clustering is an important datamining task. Several good clustering algorithms exist in the attribute-value representation. However, recent machine learning algorithms use a more expressive first order representation for the examples. In this paper we construct an AUTOCLASS-like algorithm that deals with examples that are interpretations.