Skip to main content
SpringerLink
Log in

Conclusions

  • Chapter
  • First Online:
Demand-Driven Associative Classification

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

  • 409 Accesses

Abstract

In this chapter we summarize the research contributions of this work and point out limitations and problems that remained open.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
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

References

  1. Baralis, E., Chiusano, S.: Essential classification rule sets. Trans. Database Syst. 29(4), 635–674 (2004)

    Article  Google Scholar 

  2. Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining minimal non-redundant association rules using frequent closed itemsets. In: International Conference on Computational Logic, pp. 972–986 (2000)

    Google Scholar 

  3. Boulicaut, J., Bykowski, A., Rigotti, C.: Free-sets: A condensed representation of boolean data for the approximation of frequency queries. Data Min. Knowl. Discov. 7(1), 5–22 (2003)

    Article  MathSciNet  Google Scholar 

  4. Burdick, D., Calimlim, M., Flannick, J., Gehrke, J., Yiu, T.: Mafia: A maximal frequent itemset algorithm. IEEE Trans. Knowl. Data Eng. 17(11), 1490–1504 (2005)

    Article  Google Scholar 

  5. Calders, T., Goethals, B.: Non-derivable itemset mining. Data Min. Knowl. Discov. 14(1), 171–206 (2007)

    Article  MathSciNet  Google Scholar 

  6. Chan, P., Stolfo, S.: Experiments on multistrategy learning by meta-learning. In: Proceedings of the Conference on Information and Knowledge Management (CIKM), pp. 314–323. ACM Press (1993)

    Google Scholar 

  7. Fayyad, U., Irani, K.: Multi interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1022–1027 (1993)

    Google Scholar 

  8. Gouda, K., Zaki, M.: Efficiently mining maximal frequent itemsets. In: Proceedings of the International Conference on Data Mining (ICDM). IEEE Computer Society, (2001)

    Google Scholar 

  9. Kryszkiewicz, M.:Generalized disjunction-free representation of frequent patterns with negation. J. Exp. Theor. Artif. Intell. 17(1–2), 63–82 (2005)

    Article  Google Scholar 

  10. Mehta, S., Parthasarathy, S., Yang, H.: Toward unsupervised correlation preserving discretization. Trans. Knowl. Data Eng. 17(9), 1174–1185 (2005)

    Article  Google Scholar 

  11. Muhonen, J., Toivonen, H. Closed non-derivable itemsets. In: Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pp. 601–608 (2006)

    Google Scholar 

  12. Otey, M., Parthasarathy, S., Wang, C., Veloso, A.., Meira, Jr W.: Parallel and distributed methods for incremental frequent itemset mining. Trans. Syst. Man Cybern., Part B, 34(6), 2439–2450 (2004)

    Article  Google Scholar 

  13. Otey, M.E., Wang, C., Parthasarathy, S., Veloso, A., Meira, W. Jr. : Mining frequent itemsets in distributed and dynamic databases. In: Procceedings of the International Conference on Data Mining (ICDM), IEEE Computer Society, pp. 617–620 (2003)

    Google Scholar 

  14. Veloso, A.., Meira, Jr. W., Ferreira, R., Guedes, D., Parthasarathy, S.: Asynchronous and anticipatory filter-stream based parallel algorithm for frequent itemset mining. In: Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pp. 422–433. Springer (2004)

    Google Scholar 

  15. Zaki, M.: Mining non-redundant association rules. Data Min. Knowl. Discov. 9(3), 223–248 (2004)

    Article  MathSciNet  Google Scholar 

  16. Zaki, M., Ho, C., Agrawal, R.: Parallel classification for data mining on shared-memory multiprocessors. In: Proceedings of the International Conference on Data Engineering (ICDE), IEEE Computer Society, pp. 198–205 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Universidade Federal de Minas Gerais, Antonio Carlos Av, Belo Horizonte, 6620, Brazil

    Adriano Veloso & Wagner Meira Jr.

Authors
  1. Adriano Veloso
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wagner Meira Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adriano Veloso .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Adriano Veloso

About this chapter

Cite this chapter

Veloso, A., Meira, W. (2011). Conclusions. In: Demand-Driven Associative Classification. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-0-85729-525-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-525-5_10

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-524-8

  • Online ISBN: 978-0-85729-525-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation