Abstract
Much research on inductive databases (IDBs) focuses on local models, such as item sets and association rules. In this work, we investigate how IDBs can support global models, such as decision trees. Our focus is on predictive clustering trees (PCTs). PCTs generalize decision trees and can be used for prediction and clustering, two of the most common data mining tasks. Regular PCT induction builds PCTs top-down, using a greedy algorithm, similar to that of C4.5. We propose a new induction algorithm for PCTs based on beam search. This has three advantages over the regular method: (a) it returns a set of PCTs satisfying the user constraints instead of just one PCT; (b) it better allows for pushing of user constraints into the induction algorithm; and (c) it is less susceptible to myopia. In addition, we propose similarity constraints for PCTs, which improve the diversity of the resulting PCT set.
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Kocev, D., Struyf, J., Džeroski, S. (2007). Beam Search Induction and Similarity Constraints for Predictive Clustering Trees. In: Džeroski, S., Struyf, J. (eds) Knowledge Discovery in Inductive Databases. KDID 2006. Lecture Notes in Computer Science, vol 4747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75549-4_9
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DOI: https://doi.org/10.1007/978-3-540-75549-4_9
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