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East European Conference on Advances in Databases and Information Systems

ADBIS 2010: Advances in Databases and Information Systems pp 88–102Cite as

On a Fuzzy Group-By and Its Use for Fuzzy Association Rule Mining

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6295))

Abstract

Group-by is a core database operation that is used extensively in data analysis and decision support systems. In many application scenarios, it appears useful to group values according to their compliance with a certain concept instead of founding the grouping on value equality. In this paper, we propose a new SQLf construct that supports fuzzy-partition-based group-by (FGB). We show that FGB can be used to generate fuzzy summaries as well as to mine fuzzy association rules (whose head or body are bound to a specific fuzzy value) in a practical and efficient way.

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References

  1. Tahani, V.: A conceptual framework for fuzzy query processing — a step toward very intelligent database systems. Information Processing and Management 13(5), 289–303 (1977)

    Article  MATH  Google Scholar 

  2. Bosc, P., Pivert, O.: SQLf: a relational database language for fuzzy querying. IEEE Transactions on Fuzzy Systems 3(1), 1–17 (1995)

    Article  MathSciNet  Google Scholar 

  3. Silva, Y.N., Aref, W.G., Ali, M.H.: Similarity group-by. In: Proc. of ICDE 2009, pp. 904–915 (2009)

    Google Scholar 

  4. Zadeh, L.A.: Fuzzy sets. Information and control 8(3), 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  5. Dubois, D., Prade, H.: Fundamentals of fuzzy sets. The Handbooks of Fuzzy Sets, vol. 7. Kluwer Academic Pub., Netherlands (2000)

    MATH  Google Scholar 

  6. Bosc, P., Buckles, B., Petry, F., Pivert, O.: Fuzzy databases. In: Bezdek, J., Dubois, D., Prade, H. (eds.) Fuzzy Sets in Approximate Reasoning and Information Systems. The Handbook of Fuzzy Sets Series, pp. 403–468. Kluwer Academic Publishers, Dordrecht (1999)

    Google Scholar 

  7. Dubois, D., Prade, H.: Measuring properties of fuzzy sets: a general technique and its use in fuzzy query evaluation. Fuzzy Sets and Systems 38(2), 137–152 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  8. Ruspini, E.H.: A new approach to clustering. Information and Control 15(1), 22–32 (1969)

    Article  MATH  Google Scholar 

  9. Saint-Paul, R., Raschia, G., Mouaddib, N.: General purpose database summarization. In: Proc. of VLDB 2005, pp. 733–744 (2005)

    Google Scholar 

  10. Bosc, P., Pivert, O., Liétard, L.: On the comparison of aggregates over fuzzy sets. In: Bouchon-Meunier, B., Foulloy, L., Yager, R. (eds.) Intelligent Systems for Information Processing: From Representation to Applications, pp. 141–152. Elsevier, Amsterdam (2003)

    Google Scholar 

  11. Fodor, J., Yager, R.: Fuzzy-set theoretic operators and quantifiers. In: Dubois, D., Prade, H. (eds.) Fundamentals of Fuzzy Sets. The Handbooks of Fuzzy Sets Series, vol. 1, pp. 125–193. Kluwer Academic Publishers, Dordrecht (2000)

    Google Scholar 

  12. Bosc, P., Liétard, L.: Aggregates computed over fuzzy sets and their integration into SQL. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16(6), 761–792 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  13. Bosc, P., Pivert, O.: On some fuzzy extensions of association rules. In: Proc. of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, Canada, pp. 1104–1109 (2001)

    Google Scholar 

  14. Hüllermeier, E.: Implication-based fuzzy association rules. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 241–252. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  15. Bosc, P., Pivert, O.: On two qualitative approaches to tolerant inclusion operators. Fuzzy Sets and Systems 159(21), 2786–2805 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  16. Zhang, C., Huang, Y.: Cluster by: a new SQL extension for spatial data aggregation. In: Proc. of ACM GIS, pp. 53–56 (2007)

    Google Scholar 

  17. Li, C., Wang, M., Lim, L., Wang, H., Chang, K.C.C.: Supporting ranking and clustering as generalized order-by and group-by. In: Proc. of SIGMOD 2007, pp. 127–138 (2007)

    Google Scholar 

  18. Delgado, M., Molina, C., Ariza, L.R., Sánchez, D., Miranda, M.A.V.: F-cube factory: a fuzzy olap system for supporting imprecision. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15(Suppl. 1), 59–81 (2007)

    Article  Google Scholar 

  19. Kaya, M., Alhajj, R.: Online mining of fuzzy multidimensional weighted association rules. Appl. Intell. 29(1), 13–34 (2008)

    Article  Google Scholar 

  20. Rasmussen, D., Yager, R.R.: Summary SQL – a fuzzy tool for data mining. Intell. Data Anal. 1(1-4), 49–58 (1997)

    Article  Google Scholar 

  21. Meo, R., Psaila, G., Ceri, S.: An extension to SQL for mining association rules. Data Min. Knowl. Discov. 2(2), 195–224 (1998)

    Article  Google Scholar 

  22. Clear, J., Dunn, D., Harvey, B., Heytens, M.L., Lohman, P., Mehta, A., Melton, M., Rohrberg, L., Savasere, A., Wehrmeister, R.M., Xu, M.: Nonstop SQL/MX primitives for knowledge discovery. In: Proc. of KDD 1999, pp. 425–429 (1999)

    Google Scholar 

  23. Thomas, S., Sarawagi, S.: Mining generalized association rules and sequential patterns using SQL queries. In: Proc. of KDD 1998, pp. 344–348 (1998)

    Google Scholar 

  24. Yoshizawa, T., Pramudiono, I., Kitsuregawa, M.: SQL based association rule mining using commercial RDBMS (IBM DB2 UDB EEE). In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds.) DaWaK 2000. LNCS, vol. 1874, pp. 301–306. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  25. Imielinski, T., Virmani, A.: MSQL: A query language for database mining. Data Min. Knowl. Discov. 3(4), 373–408 (1999)

    Article  Google Scholar 

  26. Rajamani, K., Cox, A.L., Iyer, B.R., Chadha, A.: Efficient mining for association rules with relational database systems. In: Proc. of IDEAS 1999, pp. 148–155 (1999)

    Google Scholar 

  27. Pereira, R., Millan, M., Machuca, F.: New algebraic operators and SQL primitives for mining association rules. In: Neural Networks and Computational Intelligence, pp. 227–232 (2003)

    Google Scholar 

  28. Rasmussen, D., Yager, R.R.: Finding fuzzy and gradual functional dependencies with SummarySQL. Fuzzy Sets and Systems 106(2), 131–142 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  29. Dubois, D., Hüllermeier, E., Prade, H.: A systematic approach to the assessment of fuzzy association rules. Data Min. Knowl. Discov. 13(2), 167–192 (2006)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Irisa – Enssat, University of Rennes 1, Technopole Anticipa, 22305, Lannion Cedex, France

    Patrick Bosc, Olivier Pivert & Grégory Smits

Authors
  1. Patrick Bosc
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  2. Olivier Pivert
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  3. Grégory Smits
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Editor information

Editors and Affiliations

  1. Department of Computer and Infoamtion Science, University of Genova, Via Dodecaneso, 35, 16146, Genova, Italy

    Barbara Catania

  2. Faculty of Science, Department of Mathematics and Informatics, University of Novi Sad, Trg Dositeja Obradovica 4, 21000, Novi Sad, Serbia

    Mirjana Ivanović

  3. Institute of Computer Science and Applied Mathematics, Christian-Albrechts-University of Kiel, Olshausenstr. 40, 24098, Kiel, Germany

    Bernhard Thalheim

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Bosc, P., Pivert, O., Smits, G. (2010). On a Fuzzy Group-By and Its Use for Fuzzy Association Rule Mining. In: Catania, B., Ivanović, M., Thalheim, B. (eds) Advances in Databases and Information Systems. ADBIS 2010. Lecture Notes in Computer Science, vol 6295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15576-5_9

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  • DOI: https://doi.org/10.1007/978-3-642-15576-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15575-8

  • Online ISBN: 978-3-642-15576-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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