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Structured Features from Concept Lattices for Unsupervised Learning and Classification

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AI 2002: Advances in Artificial Intelligence (AI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2557))

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Abstract

We present a method for identifying potentially interesting and useful concepts in a concept lattice and revising the underlying formal context and the lattice it generates to invent new descriptors and extract their definitions. This allows the re-use of concepts in an incremental way. The approach is developed using formal concept analysis and inverse resolution operators for both a theory and its lattice. A consequence of using the concept lattice to represent the concept space is that both unsupervised and supervised approaches are enabled by using different concept evaluation measures. Results are given from experiments in two standard domains with a system called Conduce which implements the method.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, Australia

    Michael Bain

Authors
  1. Michael Bain
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Editor information

Editors and Affiliations

  1. Australian Defence Force Academy, University of New South Wales, ACT 2600, Canberra, Australia

    Bob McKay

  2. Computer Science Laboratory, Australian National University, RSISE Building, ACT 0200, Canberra, Australia

    John Slaney

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© 2002 Springer-Verlag Berlin Heidelberg

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Bain, M. (2002). Structured Features from Concept Lattices for Unsupervised Learning and Classification. In: McKay, B., Slaney, J. (eds) AI 2002: Advances in Artificial Intelligence. AI 2002. Lecture Notes in Computer Science(), vol 2557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36187-1_49

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  • DOI: https://doi.org/10.1007/3-540-36187-1_49

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00197-3

  • Online ISBN: 978-3-540-36187-9

  • eBook Packages: Springer Book Archive

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