On Locality Sensitive Hashing for Sampling Extent Generators

  • Victor CodocedoEmail author
  • My Thao Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10352)


In this article we introduce a method for sampling formal concepts using locality sensitive hashing (LSH). LSH is a technique used for finding approximate nearest neighbours given a set of hashing functions. Through our approach, we are able to predict the probability of an extent in the concept lattice given set of objects and their similarity index, a generalization of the Jaccard similarity between sets. Our approach allows defining a lattice-based amplification construction to design arbitrarily discriminative sampling settings.


  1. 1.
    Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings, Compression and Complexity of Sequences (1997)Google Scholar
  2. 2.
    Buzmakov, A., Kuznetsov, S.O., Napoli, A.: Revisiting pattern structure projections. In: Baixeries, J., Sacarea, C., Ojeda-Aciego, M. (eds.) ICFCA 2015. LNCS, vol. 9113, pp. 200–215. Springer, Cham (2015). doi: 10.1007/978-3-319-19545-2_13 CrossRefGoogle Scholar
  3. 3.
    Codocedo, V., Napoli, A.: A proposition for combining pattern structures and relational concept analysis. In: Glodeanu, C.V., Kaytoue, M., Sacarea, C. (eds.) ICFCA 2014. LNCS, vol. 8478, pp. 96–111. Springer, Cham (2014). doi: 10.1007/978-3-319-07248-7_8 Google Scholar
  4. 4.
    Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS-ConceptStruct 2001. LNCS, vol. 2120, pp. 129–142. Springer, Heidelberg (2001). doi: 10.1007/3-540-44583-8_10 CrossRefGoogle Scholar
  5. 5.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  6. 6.
    Han, J., Wang, J., Lu, Y., Tzvetkov, P.: Mining top-k frequent closed patterns without minimum support. In: IEEE International Conference on Data Mining (2002)Google Scholar
  7. 7.
    Kuznetsov, S.O.: On stability of a formal concept. Ann. Math. Artif. Intell. 49(1–4), 101–115 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Kuznetsov, S.O., Obiedkov, S.A.: Comparing performance of algorithms for generating concept lattices. J. Exp. Theor. Artif. Intell. 14, 189–216 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Inria ChileSantiagoChile

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