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From Concepts to Concept Lattice: A Border Algorithm for Making Covers Explicit

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Formal Concept Analysis (ICFCA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4933))

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Abstract

The paper presents a new border algorithm for making the covering relation of concepts explicit for iceberg concept lattices. The border algorithm requires no information from the formal context relying only on the formal concept set in order to explicitly state the covering relation between formal concepts. Empirical testing is performed to compare the border algorithm with a traditional algorithm based on the Covering Edges algorithm from Concept Data Analysis [4].

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References

  1. libferris visited April 2007, http://witme.sf.net/libferris.web/

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th Int. Conf. Very Large Data Bases, VLDB, 12–15  1994, pp. 487–499. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  3. Blake, C., Merz, C.: UCI Repository of Machine Learning Databases. In: Irvine, CA: University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

  4. Carpineto, C., Romano, G.: Concept Data Analysis. Wiley, England (2004)

    MATH  Google Scholar 

  5. Ganter, B., Wille, R.: Formal Concept Analysis — Mathematical Foundations. Springer–Verlag, Berlin Heidelberg (1999)

    MATH  Google Scholar 

  6. Goethals, B., Zaki, M.J.: Advances in frequent itemset mining implementations: Report on fimi’03. In: Goethals, B., Zaki, M.J. (eds.) Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations. CEUR Workshop Proceedings, vol. 90 (2003)

    Google Scholar 

  7. Grahne, O., Zhu, J.: Efficiently using prefix-trees in mining frequent itemsets. In: Goethals, B., Zaki, M.J. (eds.) Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Ceur (2003)

    Google Scholar 

  8. Han, J.: Data mining: concepts and techniques. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  9. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Chen, W., Naughton, J., Bernstein, P.A. (eds.) 2000 ACM SIGMOD Intl. Conference on Management of Data, pp. 1–12. ACM Press, New York (2000)

    Chapter  Google Scholar 

  10. Martin, B., Eklund, P.W.: Spatial Indexing for Scalability in FCA. In: Missaoui, R., Schmid, J. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3874, pp. 205–220. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Martin, B., Eklund, P.W.: Custom asymmetric page split generalized index search trees and formal concept analysis. In: Kuznetsov, S.O., Schmidt, S. (eds.) ICFCA 2007. LNCS (LNAI), vol. 4390, Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  12. Mueller, A.: Fast sequential and parallel algorithms for association rule mining: A comparison. Technical Report CS-TR-3515, Departure of Computer Science, University of Maryland, College Park, MD (1995)

    Google Scholar 

  13. Pei, J.: Pattern-growth methods for frequent pattern mining, ph.d. thesis, computing science, simon fraser university (2001)

    Google Scholar 

  14. Pietracaprina, A., Zendolin, D.: Mining frequent itemsets using patricia tries. In: Goethals, B., Zaki, M.J. (eds.) Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Ceur (2003)

    Google Scholar 

  15. Agrawal, R., et al.: Fast discovery of association rules. In: Fayyad, U., et al. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, Menlo Park (1996)

    Google Scholar 

  16. Stumme, G., et al.: Computing iceberg concept lattices with titanic. J. on Knowledge and Data Engineering (KDE) 42, 189–222 (2002)

    Article  MATH  Google Scholar 

  17. Zaki, M.J.: Scalable algorithms for association mining. Knowledge and Data Engineering 12, 372–390 (2000)

    Article  Google Scholar 

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Raoul Medina Sergei Obiedkov

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Martin, B., Eklund, P. (2008). From Concepts to Concept Lattice: A Border Algorithm for Making Covers Explicit. In: Medina, R., Obiedkov, S. (eds) Formal Concept Analysis. ICFCA 2008. Lecture Notes in Computer Science(), vol 4933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78137-0_6

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  • DOI: https://doi.org/10.1007/978-3-540-78137-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78136-3

  • Online ISBN: 978-3-540-78137-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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