Discrete Structure Manipulation for Discovery Science Problems

  • Shin-ichi Minato
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 62)


Discovering useful knowledge from large scale database has attracted a considerable attention during the last decade. Recently, we have been working on decision diagram based large scale data processing for knowledge discovery. In most of our research work, we can observe that discrite structure manipulation is a key technique to solve many kind of real-life problems. This article presents our current and future work discrite structure manipulation for discovery science problems.


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  1. 1.
    R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, Proc. Of the 1993 ACM SIGMOD International Conference on Management of Data, Vol. 22(2) of SIGMOD Record, pages 207–216, 1993.Google Scholar
  2. 2.
    R. E. Bryant. Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Computers, C-35(8):677–691, 1986.CrossRefGoogle Scholar
  3. 3.
    B. Goethals. Survey on frequent pattern mining, 2003.
  4. 4.
    .Japan Science and Technology Agency. ERATO MINATO Discrete Structure Manipulation System Project, 10 2009.
  5. 5.
    D. E. Knuth. The Art of Computer Programming: Bitwise Tricks & Techniques; Binary Decision Diagrams, volume 4, fascicle 1. Addison-Wesley, 2009.Google Scholar
  6. 6.
    S. Minato. Zero-suppressed BDDs for set manipulation in combinatorial problems. In Proc. of 30th ACM/IEEE Design Automation Conference, pages 272–277, 1993.Google Scholar
  7. 7.
    S. Minato and T. Uno. Prequentness-transition queries for distinctive pattern min ing from time-segmented databases. In Proc. of 2010 SI AM International Confer ence on Data Mining (SDM’2010), pages 339–349, 4 2010.Google Scholar
  8. 8.
    S. Minato, T. Uno, and H. Arimura. LCM over ZBDDs: Fast generation of very large-scale frequent itemsets using a compact graph-based representation. In Proc. of 12-th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008), (LNAI 5012, Springer), pages 234–246, 5 2008.Google Scholar
  9. 9.
    T. Uno, Y. Uchida, T. Asai, and H. Arimura. LCM: an efficient algorithm for enumerating frequent closed item sets. In Proc. Workshop on Frequent Itemset Mining Implementations (FIMI’03), 2003.
  10. 10.
    M. J. Zaki. Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng., 12(2):372–390, 2000.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Shin-ichi Minato
    • 1
    • 2
  1. 1.Hokkaido UniversitySapporoJapan
  2. 2.ERATO MINATO Discrete Structure Manipulation System ProjectJapan Science and Technology AgencySapporoJapan

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