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Inductive Logic Programming
Clustering of relational data has so far received a lot less attention than classification of such data. In this paper we investigate a simple approach based on randomized propositionalization, which allows for applying standard clustering algorithms like KMeans to multi-relational data. We describe how random rules are generated and then turned into boolean-valued features. Clustering generally is not straightforward to evaluate, but preliminary experimental results on a number of standard ILPdoi:10.1007/978-3-540-78469-2_8 dblp:conf/ilp/AndersonP07 fatcat:ot3h5buwojhg3oxnn7xjsvlmiq