Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2016: Intelligent Data Engineering and Automated Learning – IDEAL 2016 pp 596–605Cite as

An Efficient Mining Algorithm for Maximal Weighted Frequent Patterns Based on WIdT-Trees

  • Conference paper
  • First Online:

  • 1808 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9937))

Abstract

As processed data is relatively dense or the support is small in weighted frequent patterns mining process, the number of frequent patterns which meet the conditions will be exponential growth, and mining all frequent patterns will need too much computation. Hence, mining the maximal weighted frequent patterns containing all frequent patterns has less calculation, and it has more utility value. Aiming at the process of maximal weighted frequent patterns mining, an efficient algorithm, based on WIdT-Trees, is proposed to discover maximal weighted frequent patterns. In the algorithm, WIdT-Tree is optimized from WIT-Tree. The dTidset strategy is used to calculate the weighted support of frequent k-itemsets, and the nodes with equal extended weighted support are pruned off in order to reduce the computational cost and decrease the search space complexity. Algorithms are tested and compared on real and synthetic datasets and experimental results show that our algorithm is more efficient and scalable.

This is a preview of subscription content, 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 EPUB and 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

Learn about institutional subscriptions

References

  1. Bandyopadhyay, S., Wolfson, J., Vock, D.M., et al.: Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data. Data Min. Knowl. Disc. 29(4), 1033–1069 (2015)

    Article  MathSciNet  Google Scholar 

  2. Solanki, S.K., Patel, J.T.: A survey on association rule mining. In: 2015 Fifth International Conference on Advanced Computing and Communication Technologies (ACCT), pp. 212–216. IEEE (2015)

    Google Scholar 

  3. Yun, U., Lee, G.: Incremental mining of weighted maximal frequent itemsets from dynamic databases. Expert Syst. Appl. 54, 304–327 (2016)

    Article  Google Scholar 

  4. Yadav, L., Nair, P.S.: A new data structure for finding maximalfrequent itemset in online data mining. In: 2015 International Conference on Computer, Communication and Control (IC4), pp. 1–5. IEEE (2015)

    Google Scholar 

  5. Zhong, Y., Liao, Y.: Research of mining effective and weighted association rules based on dual confidence. In: 2012 Fourth International Conference on Computational and Information Sciences (ICCIS), pp. 1228–1231. IEEE (2012)

    Google Scholar 

  6. Ramkumar, G.D., Ranka, S., Tsur, S.: Weighted association rules: model and algorithm. In: Proceedings of ACM SIGKDD, pp. 661–666 (1998)

    Google Scholar 

  7. Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (WAR). In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 270–274. ACM (2000)

    Google Scholar 

  8. Tao, F., Murtagh, F., Farid, M.: Weighted association rule mining using weighted support and significance framework. In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 661–666. ACM (2003)

    Google Scholar 

  9. Khan, M.S., Muyeba, M., Coenen, F.: A weighted utility framework for mining association rules. In: Second UKSIM European Symposium on Computer Modeling and Simulation, EMS 2008, pp. 87–92. IEEE (2008)

    Google Scholar 

  10. Zaki, M.J., Hsiao, C.J.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans. Knowl. Data Eng. 17(4), 462–478 (2005)

    Article  Google Scholar 

  11. Vo, B., Coenen, F., Le, B.: A new method for mining frequent weighted itemsets based on WIT-trees. Expert Syst. Appl. 40(4), 1256–1264 (2013)

    Article  Google Scholar 

  12. Tseng, V.S., Shie, B.E., Wu, C.W., et al.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013)

    Article  Google Scholar 

  13. Yun, U., Shin, H., Ryu, K.H., et al.: An efficient mining algorithm for maximal weighted frequent patterns in transactional databases. Knowl. Based Syst. 33, 53–64 (2012)

    Article  Google Scholar 

  14. Le, T., Vo, B.: MEI: an efficient algorithm for mining erasable itemsets. Eng. Appl. Artif. Intell. 27, 155–166 (2014)

    Article  Google Scholar 

  15. Borgelt, C.: Efficient implementations of apriori and eclat. In: FIMI 2003 Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (2003)

    Google Scholar 

  16. Burdick, D., Calimlim, M., Flannick, J., et al.: MAFIA: a maximal frequent itemset algorithm. IEEE Trans. Knowl. Data Eng. 17(11), 1490–1504 (2005)

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation Item of China under Grant No. 81273649, Natural Science Foundation Item of Heilongjiang Province under Grant No.F201434, and the Graduate Student Innovation and Research Item of Heilongjiang University under Grant NO.YJSCX2016-018HLJU.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Heilongjiang University, Harbin, China

    Qibing Qin & Long Tan

  2. Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Harbin, China

    Long Tan

Authors
  1. Qibing Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Long Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Long Tan .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. Nanjing University, Nanjing, China

    Yang Gao

  3. Yangzhou University, Yangzhou, Jiangsu, China

    Bin Li

  4. Aeronautics and Astronautics, Nanjing University Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. Nanjing Normal University, Nanjing, China

    Ming Yang

  6. Yangzhou University, Yangzhou, Jiangsu, China

    Yun Li

  7. Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Qin, Q., Tan, L. (2016). An Efficient Mining Algorithm for Maximal Weighted Frequent Patterns Based on WIdT-Trees. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46257-8_64

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46256-1

  • Online ISBN: 978-3-319-46257-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation