Abstract
The two most commonly addressed data mining tasks are predictive modelling and clustering. Here we address the task of predictive clustering, which contains elements of both and generalizes them to some extent. Predictive clustering has been mainly evaluated in the context of trees. In this paper, we extend predictive clustering toward rules. Each cluster is described by a rule and different clusters are allowed to overlap since the sets of examples covered by different rules do not need to be disjoint. We propose a system for learning these predictive clustering rules, which is based on a heuristic sequential covering algorithm. The heuristic takes into account both the precision of the rules (compactness w.r.t. the target space) and the compactness w.r.t. the input space, and the two can be traded-off by means of a parameter. We evaluate our system in the context of several multi-objective classification problems.
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Ženko, B., Džeroski, S., Struyf, J. (2006). Learning Predictive Clustering Rules. In: Bonchi, F., Boulicaut, JF. (eds) Knowledge Discovery in Inductive Databases. KDID 2005. Lecture Notes in Computer Science, vol 3933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11733492_14
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DOI: https://doi.org/10.1007/11733492_14
Publisher Name: Springer, Berlin, Heidelberg
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