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Dependency and Granularity in Data -Mining

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Computational Complexity
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Article Outline

Definition of the Subject

Introduction

Contingency Table from Rough Sets

Rank of Contingency Table (2 × 2)

Rank of Contingency Table (m × n)

Rank and Degree of Dependence

Degree of Granularity and Dependence

Conclusion

Acknowledgment

Bibliography

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Notes

  1. 1.

    The threshold  δ is the degree of the closeness of overlapping sets, which will be given by domain experts. For more information, please refer to Sect. “Rank of Contingency Table (\( { {2 \times 2} } \))”.

Bibliography

  1. Butz C (2002) Exploiting contextual independencies in web search and user profiling. In: Proceedings of World Congress on Computational Intelligence (WCCI'2002) (CD-ROM)

    Google Scholar 

  2. Pawlak Z (1991) Rough sets. Kluwer, Dordrecht

    Book  MATH  Google Scholar 

  3. Skowron A, Grzymala-Busse J (1994) From rough set theory to evidence theory. In: Yager R, Fedrizzi M, Kacprzyk J (eds) Advances in the Dempster–Shafer Theory of Evidence. Wiley, New York, pp 193–236

    Google Scholar 

  4. Tsumoto S (2000) Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic. Inf Sci 124:125–137

    Article  Google Scholar 

  5. Tsumoto S (2003) Statistical independence as linear independence. In: Skowron A, Szczuka M (eds) Electronic Notes in Theoretical Computer Science, vol 82. Elsevier

    Google Scholar 

  6. Tsumoto S, Tanaka H (1996) Automated discovery of medical expert system rules from clinical databases based on rough sets. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining 96. AAAI Press, Palo Alto, pp 63–69

    Google Scholar 

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Acknowledgment

This work was supported by the Grant-in-Aid for Scientific Research (13131208) on PriorityAreas (No.759) “Implementation of Active Mining in the Era of Information Flood” by the Ministry of Education, Science, Culture, Sports,Science and Technology of Japan.

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© 2012 Springer-Verlag

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Tsumoto, S., Hirano, S. (2012). Dependency and Granularity in Data -Mining . In: Meyers, R. (eds) Computational Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1800-9_54

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