Skip to main content
SpringerLink
Log in

An Investigation of Objective Interestingness Measures for Association Rule Mining

  • Conference paper
  • First Online:
PRICAI 2016: Trends in Artificial Intelligence (PRICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9810))

Included in the following conference series:

Abstract

While a large number of objective interestingness measures have been proposed to describe an association pattern which encodes meaningful relationship among attributes in a dataset, their characteristics and interrelations are not well explored. In this work, we investigate static and dynamic characteristics of 21 commonly used interestingness measures in order to understand their common and distinct properties. Four systematical methods investigated are (1) trend analysis, (2) fixed-total variable-portion analysis, (3) fixed-total fixed-portion-combination analysis, and (4) imbalance and extreme scenario analysis. A correlation analysis has been made to find interrelation patterns of the measures.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD International Conference on Management of Data, Washington DC, USA, pp. 207–216 (1993)

    Google Scholar 

  2. Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: ACM SIGMOD International Conference on Management of Data. New York, USA, 255–264 (1997)

    Google Scholar 

  3. Leung, K.S., Wong, K.C., Chan, T.M., Wong, M.H., Lee, K.H., Lau, C.K., Tsui, S.K.: Discovering protein–DNA binding sequence patterns using association rule mining. Nucleic Acids Res. 38(19), 6324–6337

    Google Scholar 

  4. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web usage mining: discovery and applications of usage patterns from web data. ACM SIGKDD Explor. Newsl. 1(2), 12–23 (2000)

    Article  Google Scholar 

  5. Lee, W., Stolfo, S.J., Mok, K.W.: A data mining framework for building intrusion detection models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 120–132. IEEE (1999)

    Google Scholar 

  6. Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–238 (1991)

    Google Scholar 

  7. Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, pp. 32–41 (2002)

    Google Scholar 

  8. Agresti, A.: Categorical Data Analysis. Wiley, New York (1990)

    MATH  Google Scholar 

  9. Goodman, L.A., Kruskal, W.H.: Measures of associationfor cross-classifications. J. Am. Stat. Assoc. 49, 732–764 (1968)

    MATH  Google Scholar 

  10. Mosteller, J.: Association and estimation in contingency tables. J. Am. Stat. Assoc. 63, 1–28 (1968)

    MathSciNet  Google Scholar 

  11. Yule, G.U.: On the methods of measuring association between two attributes. J. R. Stat. Soc. 75, 579–642 (1912)

    Article  Google Scholar 

  12. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960)

    Article  Google Scholar 

  13. Cover, T., Thomas, J.: Elements of Information Theory. Wiley, New York (1991)

    Book  MATH  Google Scholar 

  14. Smyth, P., Goodman, R.M.: Rule induction using information theory. In: Shapiro, G.P., Frawley, W. (eds.) Knowledge Discovery in Databases, pp. 159–176. MIT Press, Cambridge (1991)

    Google Scholar 

  15. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Chapman & Hall, New York (1984)

    MATH  Google Scholar 

  16. Clark, P., Boswell, R.: Rule induction with cn2: some recent improvements. In: Proceedings of the European Working Session on Learning EWSL-91, Porto, Portugal, pp. 151–163 (1991)

    Google Scholar 

  17. Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of 1997 ACM-SIGMOD International Conference on Management of Data, Montreal, Canada, pp. 255–264 (1997)

    Google Scholar 

  18. DuMouchel, W., Pregibon, D.: Empirical bayes screening for multi-item associations. In: The Seventh International Conference on Knowledge Discovery and Data Mining, pp. 67–76 (2001)

    Google Scholar 

  19. Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Math. Biosci. 23, 351–379 (1975)

    Article  MathSciNet  Google Scholar 

  20. Tan, P.N., Kumar, V.: Interestingness measures for association patterns: a perspective. In: KDD 2000 Workshop on Post-processing in Machine Learning and Data Mining, Boston, MA, August (2000)

    Google Scholar 

  21. van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)

    MATH  Google Scholar 

  22. Klosgen, W.: Problems for knowledge discovery in databases and their treatment in the statistics interpreter explora. Int. J. Intell. Syst. 7(7), 649–673 (1992)

    Article  MATH  Google Scholar 

Download references

Acknowledgement

This work has been supported funding by Rangsit University and Sirindhorn International Institute of Technology, Thammasat University.

Author information

Authors and Affiliations

  1. Department of Medical Informatics, College of Information and Communication Technology, Rangsit University, Lak Hok, Thailand

    Ratchasak Somyanonthanakul

  2. Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand

    Thanaruk Theeramunkong

Authors
  1. Ratchasak Somyanonthanakul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thanaruk Theeramunkong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ratchasak Somyanonthanakul .

Editor information

Editors and Affiliations

  1. Cardiff University, Cardiff, United Kingdom

    Richard Booth

  2. Southeast University , Nanjing, China

    Min-Ling Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Somyanonthanakul, R., Theeramunkong, T. (2016). An Investigation of Objective Interestingness Measures for Association Rule Mining. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42911-3_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation