Skip to main content

A Space-Time Trade Off for FUFP-trees Maintenance

  • Conference paper
Intelligent Information and Database Systems (ACIIDS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7803))

Included in the following conference series:

Abstract

In the past, Hong et al. proposed an algorithm to maintain the fast updated frequent pattern tree (FUFP-tree), which was an efficient data structure for association-rule mining. However in the maintenance process, the counts of infrequent items and the IDs of transactions with those items were determined by rescanning all the transactions in the original database. This step might be quite time-consuming depending on the number of transactions in the original database and the number of rescanned items. This study improves that approach by storing 1-items during the maintenance process and based on the properties of FUFP-trees, such that the rescanned items and inserted items are processed more efficiently to reduce execution time. Experimental results show that the improved algorithm needs some more memory to store infrequent 1-items but the performance is better than the original one.

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

Access this chapter

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.: Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5(6), 914–925 (1993)

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The 20th International Conference on Very Large Databases, pp. 487–499 (1994)

    Google Scholar 

  3. Agrawal, R., Srikant, R., Vu, Q.: Mining association rules with item constraints. In: The Third International Conference on Knowledge Discovery in Databases and Data Mining, pp. 67–73 (1997)

    Google Scholar 

  4. Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Mining optimized association rules for numeric attributes. In: The ACM Sigact-Sigmod Symposium on Principles of Database Systems, pp. 182–191 (1996)

    Google Scholar 

  5. Han, J., Fu, Y.: Discovery of multiple-level association rules from large database. In: The Twenty-first International Conference on Very Large Data Bases, pp. 420–431 (1995)

    Google Scholar 

  6. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD Conference, pp. 1–12 (2000)

    Google Scholar 

  7. Hong, T.P., Lin, C.W., Wu, Y.L.: Maintenance of fast updated frequent pattern trees for record deletion. Computational Statistics & Data Analysis 53(7), 2485–2499 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Hong, T.P., Lin, C.W., Wu, Y.L.: Incrementally fast updated frequent pattern trees. Expert Systems with Applications 34(4), 2424–2435 (2008)

    Article  Google Scholar 

  9. Lin, C.W., Hong, T.P., Wu, Y.L.: The Pre-FUFP algorithm for incremental mining. Expert Systems with Applications 36(5), 9498–9505 (2009)

    Article  Google Scholar 

  10. Mannila, H., Toivonen, H., Verkamo, A.I.: Efficient algorithm for discovering association rules. In: The AAAI Workshop on Knowledge Discovery in Databases, pp. 181–192 (1994)

    Google Scholar 

  11. Park, J.S., Chen, M.S., Yu, P.S.: Using a hash-based method with transaction trimming for mining association rules. IEEE Transactions on Knowledge and Data Engineering 9(5), 812–825 (1997)

    Article  Google Scholar 

  12. Vo, B., Le, B.: Mining minimal non-redundant association rules using frequent itemsets lattice. Journal of Intelligent Systems Technology and Applications 10(1), 92–106 (2011)

    Article  Google Scholar 

  13. Vo, B., Le, B.: Interestingness for association rules: Combination between lattice and hash tables. Expert Systems with Applications 38(9), 11630–11640 (2011)

    Article  Google Scholar 

  14. Le, B., Nguyen, H., Vo, B.: An efficient strategy for mining high utility itemsets. International Journal of Intelligent Information and Database Systems 5(2), 164–176 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Le, B., Tran, CT., Hong, TP., Vo, B. (2013). A Space-Time Trade Off for FUFP-trees Maintenance. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36543-0_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-36543-0_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36542-3

  • Online ISBN: 978-3-642-36543-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics