Skip to main content
SpringerLink
Log in

Extracting Mutually Dependent Multisets

  • Conference paper
  • First Online:
Discovery Science (DS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10558))

Included in the following conference series:

Abstract

In this paper, we extend mutually dependent patterns as itemsets introduced by Ma and Hellerstein (2001) to mutually dependent multisets allowing two or more occurrences of the same items. Then, by improving the algorithm to extract all of the mutually dependent patterns based on Apriori with maintaining itemsets and their supports, we design the algorithm to extract all of the mutually dependent multisets based on AprioriTid with traversing a database just once and maintaining both multisets and their tail occurrences but without computing overall multiplicity of items in multisets. Finally, we give experimental results to apply the algorithm to both real data as antibiograms consisting of a date, a patient id, a detected bacterium, and so on and artificial data obtained by repeating items in transaction data.

This work is partially supported by Grant-in-Aid for Scientific Research 17H00762, 16H02870, 16H01743 and 15K12102 from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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. Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: Proceedings of VLDB 1994, pp. 487–499 (1994)

    Google Scholar 

  2. Frequent itemset mining implementation repository (FIMI repository). http://www.fimi.ua.ac.be/data

  3. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns withoout candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8, 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  4. Li, T. (ed.): Event Mining: Algorithms and Applications. CRC Press, Boca Raton (2015)

    Google Scholar 

  5. Ma, S., Hellerstein, J.L.: Mining mutually dependent patterns. In: Proceedings of ICDM 2001, pp. 409–416 (2001)

    Google Scholar 

  6. Nagayama, K., Hirata, K., Yokoyama, S., Matsuoka, K.: Extracting propagation patterns from bacterial culture data in medical facility. In: Otake, M., Kurahashi, S., Ota, Y., Satoh, H., Bekki, D. (eds.) New Frontiers in Artificial Intelligence. LNCS, vol. 10091, pp. 409–417. Springer, Cham (2017)

    Chapter  Google Scholar 

  7. Omiecinski, E.: Alternative interest measures for mining associations. IEEE Trans. Knowl. Data Eng. 15, 57–69 (2003)

    Article  Google Scholar 

  8. Xiong, H., Tan, P.-N., Kumer, V.: Hyperclique pattern discovery. Data Min. Knowl. Discov. 13, 219–242 (2006)

    Article  MathSciNet  Google Scholar 

  9. Zaki, H.J., Meira Jr., W.: Data Mining and Analysis. Cambridge University Press, New York (2014)

    MATH  Google Scholar 

Download references

Acknowledgment

The authors would like to thank anomymous refrees of DS2017 for valuable comments to revise the submitted version of this paper.

Author information

Authors and Affiliations

  1. Department of Artificial Intelligence, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan

    Natsuki Kiyota & Kouichi Hirata

  2. Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan

    Sho Shimamura

Authors
  1. Natsuki Kiyota
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sho Shimamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kouichi Hirata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kouichi Hirata .

Editor information

Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Akihiro Yamamoto

  2. Hokkaido University, Sapporo, Japan

    Takuya Kida

  3. National Institute of Informatics, Tokyo, Japan

    Takeaki Uno

  4. Gakushuin University, Tokyo, Japan

    Tetsuji Kuboyama

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kiyota, N., Shimamura, S., Hirata, K. (2017). Extracting Mutually Dependent Multisets. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds) Discovery Science. DS 2017. Lecture Notes in Computer Science(), vol 10558. Springer, Cham. https://doi.org/10.1007/978-3-319-67786-6_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67786-6_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67785-9

  • Online ISBN: 978-3-319-67786-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation