Skip to main content
SpringerLink
Log in

Exploring Fuzzy Ontologies in Mining Generalized Association Rules

  • Conference paper
Computational Science and Its Applications – ICCSA 2012 (ICCSA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7335))

Included in the following conference series:

  • 2350 Accesses

Abstract

The most common use of fuzzy taxonomies in mining generalized association rules occurs in the pre-processing stage, through the concept of extended transaction. A related problem is that extended transactions lead to the generation of huge amount of candidates and rules. Beyond that, the inclusion of ancestors may to generate redundancy problems. Besides, it is possible to see that the works have only assumed the total relation between database items and taxonomy nodes. The total relation occurs when all structure items have an equivalent representative item in the dataset, and vice-versa. Furthermore, the works have been directing for the question of mining fuzzy rules, exploring linguistic terms, but few approaches have explored new steps of the mining process. In this sense, this paper proposes the extended FOntGAR algorithm, an algorithm for mining generalized association rules under all levels of fuzzy ontologies, where the relation between database items and ontology items do not need be total. In this work the generalization is done during the post-processing step.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases, Washington, DC, USA, pp. 207–216 (1993)

    Google Scholar 

  2. Srikant, R., Agrawal, R.: Mining Generalized Association Rules. Presented at the Proceedings of the 21th International Conference on Very Large Data Bases (1995)

    Google Scholar 

  3. Han, J., Fu, Y.: Discovery of Multiple-Level Association Rules from Large Databases. Presented at the 21 VLDB Conference, Zurich, Switzerland (1995)

    Google Scholar 

  4. Carvalho, V.O.D., Rezende, S.O., Castro, M.D.: Obtaining and evaluating generalized association rules. In: 9th International Conference on Enterprise Information Systems, ICEIS 2007, Funchal, Madeira, June 12-16, pp. 310–315 (2007)

    Google Scholar 

  5. Wei, Q., Chen, G.: Mining generalized association rules with fuzzy taxonomic structures. In: 18th International Conference of the North American Fuzzy Information Processing Society, NAFIPS 1999, pp. 477–481 (1999)

    Google Scholar 

  6. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  7. Lee, J.-H., Lee-Kwang, H.: An Extension of Association Rules Using Fuzzy Sets. Presented at the 7th International Fuzzy Systems Association World Congress, Prague, Czech (1997)

    Google Scholar 

  8. Wu, C.-M., Huang, Y.-F.: Generalized association rule mining using an efficient data structure. Expert Systems with Applications 38, 7277–7290 (2011)

    Article  Google Scholar 

  9. Vo, B., Le, B.: Fast Algorithm for Mining Generalized Association Rules. International Journal of Database Theory and Application 2, 1–12 (2009)

    Google Scholar 

  10. Hung-Pin, C., Yi-Tsung, T., Kun-Lin, H.: A Cluster-Based Method for Mining Generalized Fuzzy Association Rules. In: First International Conference on Innovative Computing, Information and Control, ICICIC 2006, pp. 519–522 (2006)

    Google Scholar 

  11. Mahmoudi, E.V., Sabetnia, E., Torshiz, M.N., Jalali, M., Tabrizi, G.T.: Multi-level Fuzzy Association Rules Mining via Determining Minimum Supports and Membership Functions. In: 2011 Second International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 55–61 (2011)

    Google Scholar 

  12. Cai, C.H., Fu, A.W.C., Cheng, C.H., Kwong, W.W.: Mining Association Rules with Weighted Items. In: International Database Engineering and Application Symposium, pp. 68–77 (1998)

    Google Scholar 

  13. Hong, T.P., Lin, K.Y., Wang, S.L.: Fuzzy data mining for interesting generalized association rules. Fuzzy Sets and Systems 138, 255–269 (2003)

    Article  MathSciNet  Google Scholar 

  14. Lee, Y.-C., Hong, T.-P., Wang, T.-C.: Multi-level fuzzy mining with multiple minimum supports. Expert Systems with Applications: An International Journal 34, 459–468 (2008)

    Article  Google Scholar 

  15. Adomavicius, G., Tuzhilin, A.: Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery 5, 33–58 (2001)

    Article  MATH  Google Scholar 

  16. Angryk, R.A., Petry, F.E.: Mining Multi-Level Associations with Fuzzy Hierarchies. In: The 14th IEEE International Conference on Fuzzy Systems, Reno, NV, pp. 785–790 (2005)

    Google Scholar 

  17. Wen-Yang, L., Ming-Cheng, T., Ja-Hwung, S.: Updating generalized association rules with evolving fuzzy taxonomies. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–6 (2010)

    Google Scholar 

  18. Chen, G., Wei, Q.: Fuzzy association rules and the extended mining algorithms. Information Sciences - Informatics and Computer Science: An International Journal 147, 201–228 (2002)

    MathSciNet  MATH  Google Scholar 

  19. Escovar, E.L.G., Yaguinuma, C.A., Biajiz, M.: Using Fuzzy Ontologies to Extend Semantically Similar Data Mining. Presented at the 21 Brazilian Symposium on Databases, Florianópolis, Brazil (2006)

    Google Scholar 

  20. Miani, R.G., Yaguinuma, C.A., Santos, M.T.P., Biajiz, M.: NARFO Algorithm: Mining Non-redundant and Generalized Association Rules Based on Fuzzy Ontologies. In: Filipe, J., Cordeiro, J. (eds.) ICEIS 2009. LNBIP, vol. 24, pp. 415–426. Springer, Heidelberg (2009)

    Google Scholar 

  21. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. Presented at the Conference on Very Large Databases (VLDB), Santiago, Chile (1994)

    Google Scholar 

  22. Yaguinuma, C., Santos, M., Biajiz, M.: Fuzzy Meta-ontology for Representation of Imprecise Information in Ontologies. Presented at the II WOMSDE (2007)

    Google Scholar 

  23. Smith, M.K., Welt, C., McGuinness, D.L.: W3C Proposed Recomendation: OWL Web Ontology Language Guide (Dezembro 2, 2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Federal University of Sao Carlos - UFSCar, Sao Carlos, Brazil

    Rodrigo Moura Juvenil Ayres, Marcela Xavier Ribeiro & Marilde Terezinha Prado Santos

Authors
  1. Rodrigo Moura Juvenil Ayres
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcela Xavier Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marilde Terezinha Prado Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Laboratory of Urban and Territorial Systems, University of Basilicata, 10, Viale dell’Ateneo Lucano, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  3. Department of Cyber Security Science, Federal University of Technology, Gidan Kwano Campus, Minna, Nigeria

    Sanjay Misra

  4. Faculty of Engineering, Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rua Sao Francisco Xavier, 524, 50. andar, sala 5145-F, Maracana, 20.550-013, Rio de Janeiro, RJ, Brazil

    Nadia Nedjah

  5. Department of Production and Systems, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, Higashi-ku, 813-8503, Fukuoka, Japan

    Bernady O. Apduhan

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ayres, R.M.J., Ribeiro, M.X., Santos, M.T.P. (2012). Exploring Fuzzy Ontologies in Mining Generalized Association Rules. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31137-6_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31137-6_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31136-9

  • Online ISBN: 978-3-642-31137-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation