Skip to main content
SpringerLink
Log in

Exploiting High Utility Occupancy Patterns

  • Conference paper
  • First Online:
Book cover Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10366))

Abstract

Most studies have considered the frequency as sole interestingness measure for identifying high quality patterns. However, each object is different in nature, in terms of criteria such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, and may not be truly representative. Thus, this paper extends the occupancy measure to assess the utility of patterns in transaction databases. The High Utility Occupancy Pattern Mining (HUOPM) algorithm considers user preferences in terms of frequency, utility, and occupancy. Several novel data structures are designed to discover the complete set of high quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of HUOPM.

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 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

Institutional subscriptions

References

  1. Frequent itemset mining dataset repository (2012). http://fimi.ua.ac.be/data/

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

    Google Scholar 

  3. Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Le, Y.K.: Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng. 21(12), 1708–1721 (2009)

    Article  Google Scholar 

  4. Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: International Conference on Data Mining, pp. 19–26 (2003)

    Google Scholar 

  5. Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54–77 (2017)

    Google Scholar 

  6. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  7. Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)

    Google Scholar 

  8. Lin, J.C.W., Gan, W., Fournier-Viger, P., Hong, T.P., Tseng, V.S.: Efficient algorithms for mining high-utility itemsets in uncertain databases. Knowl. Based Syst. 96, 171–187 (2016)

    Article  Google Scholar 

  9. Yao, H., Hamilton, J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: SIAM International Conference on Data Mining, pp. 211–225 (2004)

    Google Scholar 

  10. Rymon, R.: Search through systematic set enumeration. Technical reports (CIS), p. 297 (1992)

    Google Scholar 

  11. Shen, B., Wen, Z., Zhao, Y., Zhou, D., Zheng, W.: OCEAN: fast discovery of high utility occupancy itemsets. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 354–365 (2016)

    Google Scholar 

  12. Tang, L., Zhang, L., Luo, P., Wang, M.: Incorporating occupancy into frequent pattern mining for high quality pattern recommendation. In: Proceedings of the 21st ACM International Conference on Information and knowledge management, pp. 75–84 (2012)

    Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by the National Natural Science Foundation of China (NSFC) under grant No. 61503092, by the Research on the Technical Platform of Rural Cultural Tourism Planning Basing on Digital Media under grant 2017A020220011, and by the Tencent Project under grant CCF-Tencent IAGR20160115.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Wensheng Gan, Jerry Chun-Wei Lin & Han-Chieh Chao

  2. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Philippe Fournier-Viger

  3. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Han-Chieh Chao

Authors
  1. Wensheng Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jerry Chun-Wei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philippe Fournier-Viger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Han-Chieh Chao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jerry Chun-Wei Lin .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University, Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Gan, W., Lin, J.CW., Fournier-Viger, P., Chao, HC. (2017). Exploiting High Utility Occupancy Patterns. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63579-8_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63578-1

  • Online ISBN: 978-3-319-63579-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation