Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
R. E. Bryant. Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Computers, C-35(8):677–691, 1986.
B. Goethals. Survey on frequent pattern mining, 2003. http://www.cs.helsinki.fi/u/goethals/publications/survey.ps.
.Japan Science and Technology Agency. ERATO MINATO Discrete Structure Manipulation System Project, 10 2009. http://www.jst.go.jp/erato/project/mrk_P/mrk_P.html.
D. E. Knuth. The Art of Computer Programming: Bitwise Tricks & Techniques; Binary Decision Diagrams, volume 4, fascicle 1. Addison-Wesley, 2009.
S. Minato. Zero-suppressed BDDs for set manipulation in combinatorial problems. In Proc. of 30th ACM/IEEE Design Automation Conference, pages 272–277, 1993.
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.
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.
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. http://fimi.cs.helsinki.fi/src/.
M. J. Zaki. Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng., 12(2):372–390, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media B.V.
About this paper
Cite this paper
Minato, Si. (2011). Discrete Structure Manipulation for Discovery Science Problems. In: Gelenbe, E., Lent, R., Sakellari, G., Sacan, A., Toroslu, H., Yazici, A. (eds) Computer and Information Sciences. Lecture Notes in Electrical Engineering, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9794-1_67
Download citation
DOI: https://doi.org/10.1007/978-90-481-9794-1_67
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-9793-4
Online ISBN: 978-90-481-9794-1
eBook Packages: EngineeringEngineering (R0)