Abstract
We present The Cruncher, a simple representation framework and algorithm based on minimum description length for automatically forming an ontology of concepts from attribute-value data sets. Although unsupervised, when The Cruncher is applied to an animal data set, it produces a nearly zoologically accurate categorization. We demonstrate The Cruncher’s utility for finding useful macro-actions in Reinforcement Learning, and for learning models from uninterpreted sensor data. We discuss advantages The Cruncher has over concept lattices and hierarchical clustering.
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© 2005 Springer-Verlag Berlin Heidelberg
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Pickett, M., Oates, T. (2005). The Cruncher: Automatic Concept Formation Using Minimum Description Length. In: Zucker, JD., Saitta, L. (eds) Abstraction, Reformulation and Approximation. SARA 2005. Lecture Notes in Computer Science(), vol 3607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527862_21
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DOI: https://doi.org/10.1007/11527862_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27872-6
Online ISBN: 978-3-540-31882-8
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