Clustering Multi-relationnal TV Data by Diverting Supervised ILP

Traditionally, clustering operates on data described by a fixed number of (usually numerical) features; this description schema is said propositional or attribute-value. Yet, when the data cannot be described in that way, usual data-mining or clustering algorithms are no longer suitable. In this paper, we consider the problem of discovering similar types of programs in TV streams. The TV data have two important characteristics: 1) they are multi-relational, that is to say with multiple relationships between features; 2) they require background knowledge external to their interpretation. To process the data, we use Inductive Logic Programming (ILP) [9]. In this paper, we show how to divert ILP to work unsupervised in this context: from artificial learning problems, we induce a notion of similarity between broadcasts, which is later used to perform the clustering. Experiments presented show the soundness of the approach, and thus open up many research avenues.

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