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Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-Acid Data

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Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

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Abstract

The paper proposes a way of peculiarity oriented mining and its application for knowledge discovery in the amino-acid data set. We introduce the peculiarity rules as a new type of association rules, which can be discovered from a relatively small number of peculiar data by searching the relevance among the peculiar data. We argue that the peculiarity rules represent a typically unexpected, interesting regularity hidden in the amino-acid data set.

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References

  1. Agrawal R. et al. “Database Mining: A Performance Perspective”, IEEE Trans. Knowl. Data Eng., 5(6) (1993) 914–925.

    Article  Google Scholar 

  2. Agrawal R. et al. “Fast Discovery of Association Rules”, Advances in Knowledge Discovery and Data Mining, AAAI Press (1996) 307–328.

    Google Scholar 

  3. Fayyad, U.M., Piatetsky-Shapiro, G et al (eds.) Advances in Knowledge Discovery and Data Mining, AAAI Press (1996).

    Google Scholar 

  4. Freitas, A.A. “On Objective Measures of Rule Surprisingness” J. Zytkow and M. Quafafou (eds.) Principles of Data Mining and Knowledge Discovery, LNAI 1510, Springer (1998) 1–9.

    Google Scholar 

  5. Johnson, R.A. and Wichern, D.W. Applied Multivariate Statistical Analysis, Prentice Hall (1998).

    Google Scholar 

  6. Lin, T.Y. “Granular Computing on Binary Relations 1: Data Mining and Neighborhood Systems”, L. Polkowski and A. Skowron (eds.) Rough Sets in Knowledge Discovery 1, In Studies in Fuzziness and Soft Computing series, Vol. 18, Physica-Verlag (1998) 107–121.

    Google Scholar 

  7. Lin, T.Y., Zhong, N., Dong, J., and Ohsuga, S. “Frameworks for Mining Binary Relations in Data”, L. Polkowski and A. Skowron (eds.) Rough Sets and Current Trends in Computing, LNAI 1424, Springer (1998) 387–393.

    Google Scholar 

  8. Liu, B., Hsu W., and Chen, S. “Using General Impressions to Analyze Discovered Classification Rules”, Proc. Third International Conference on Knowledge Discovery and Data Mining (KDD-97), AAAI Press (1997) 31–36.

    Google Scholar 

  9. Silberschatz, A. and Tuzhilin, A. “What Makes Patterns Interesting in Knowledge Discovery Systems”, IEEE Trans. Knowl. Data Eng., 8(6) (1996) 970–974.

    Article  Google Scholar 

  10. Suzuki E.. “Autonomous Discovery of Reliable Exception Rules”, Proc Third Inter. Conf. on Knowledge Discovery and Data Mining (KDD-97), AAAI Press (1997) 259–262.

    Google Scholar 

  11. Thrun, S. et al. “Automated Learning and Discovery”, AI Magazine (Fall 1999) 78–82.

    Google Scholar 

  12. Tsumoto, K. and Kumagai, I. “Thermodynamic and Kinetic Analyses of The Antigen-Antibody Interaction Using Mutants”, Research Report of JSAI SIG-KBS-A002 (2000) 83–88.

    Google Scholar 

  13. Wrobel, S. “An Algorithm for Multi-relational Discovery of Subgroups”, J. Komorowski and J. Zytkow (eds.) Principles of Data Mining and Knowledge Discovery. LNAI 1263, Springer (1997) 367–375.

    Google Scholar 

  14. Yao, Y.Y. “Granular Computing using Neighborhood Systems”, Roy, R., Furuhashi, T., and Chawdhry, P.K. (eds.) Advances in Soft Computing: Engineering Design and Manufacturing, Springer (1999) 539–553.

    Google Scholar 

  15. Yao, Y.Y. and Zhong, N. “Potential Applications of Granular Computing in Knowledge Discovery and Data Mining”, Proc. The 5th International Conference on Information Systems Analysis and Synthesis (IASA’99), edited in the invited session on Intelligent Data Mining and Knowledge Discovery (1999) 573–580.

    Google Scholar 

  16. Zadeh, L.A. “Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic”, Fuzzy Sets and Systems, Elsevier, 90 (1997) 111–127.

    Google Scholar 

  17. Zhong, N. and Ohsuga, S. “KOSI-An Integrated System for Discovering Functional Relations from Databases”, Journal of Intelligent Information Systems, Vol.5,No.1, Kluwer (1995) 25–50.

    Article  Google Scholar 

  18. Zhong, N., Yao, Y.Y., and Ohsuga, S. “Peculiarity Oriented Multi-Database Mining”, J. Zytkow and Jan Rauch (eds.) Principles of Data Mining and Knowledge Discovery. LNAI 1704, Springer (1999) 136–146.

    Google Scholar 

  19. Zhong, N., Skowron, A., and Ohsuga, S. (eds.) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, LNAI 1711, Springer (1999).

    Google Scholar 

  20. Zhong, N. “MULTI-DATABASE MINING: A Granular Computing Approach”, Proc. 5th Joint Conference on Information Sciences (JCIS’00) in special session on Granular Computing and Data Mining (GrC-DM) (2000) 198–201.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Information Eng., Maebashi Institute of Technology, Japan

    Ning Zhong & Muneaki Ohshima

  2. Dept. of Information and Computer Science, Waseda University, Japan

    Setsuo Ohsuga

Authors
  1. Ning Zhong
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  2. Muneaki Ohshima
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  3. Setsuo Ohsuga
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Zhong, N., Ohshima, M., Ohsuga, S. (2001). Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-Acid Data. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_29

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  • DOI: https://doi.org/10.1007/3-540-45357-1_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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