Skip to main content
Springer Nature Link
Log in

Efficient Detection of Local Interactions in the Cascade Model

  • Conference paper
  • First Online:
Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

Included in the following conference series:

Abstract

Detection of interactions among data items constitutes an essential part of knowledge discovery. The cascade model is a rule induction methodology using levelwise expansion of a lattice. It can detect positive and negative interactions using the sum of squares criterion for categorical data. An attribute-value pair is expressed as an item, and the BSS (between-groups sum of squares) value along a link in the itemset lattice indicates the strength of interaction among item pairs. A link with a strong interaction is represented as a rule. Items on the node constitute the left-hand side (LHS) of a rule, and the right-hand side (RHS) displays veiled items with strong interactions with the added item. This implies that we do not need to generate an itemset containing the RHS items to get a rule. This property enables effective rule induction. That is, rule links can be dynamically detected during the generation of a lattice. Furthermore, the BSS value of the added attribute gives an upper bound to those of other attributes along the link. This property gives us an effective pruning method for the itemset lattice. The method was implemented as the software DISCAS. There, the items to appear in the LHS and RHS are easily controlled by input parameters. Its algorithms are depicted and an application is provided as an illustrative example.

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

Access this chapter

Log in via an institution

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.: Mining Association Rules between Sets of Items in Large Databases. Proc. ACM SIGMOD (1993) 207–216

    Google Scholar 

  2. Ali, K., Manganaris, S., Srikant, R.: Partial Classification using Association Rules. Proc. KDD-97 (1997) 115–118

    Google Scholar 

  3. Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. Proc. KDD-98 (1998) 80–86

    Google Scholar 

  4. Meretakis, D., Wüthrich, B.: Classification as Mining and Use of Labeled Itemsets. Proc. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (1999)

    Google Scholar 

  5. Silverstein, C., Brin, S., Motwani, R.: Beyond Market Baskets: Generalizing Association Rules to Dependence Rules. Data Mining and Knowledge Discovery, 2 (1998) 39–68

    Article  Google Scholar 

  6. Okada, T.: Finding Discrimination Rules using the Cascade Model. J. Jpn. Soc. Artificial Intelligence, 15 (2000) in press

    Google Scholar 

  7. Okada, T.: Rule Induction in Cascade Model based on Sum of Squares Decomposition. Principles of Data Mining and Knowledge Discovery (Proc. PKDD’99), 468–475, Lecture Notes in Artificial Intelligence 1704, Springer-Verlag (1999).

    Google Scholar 

  8. Okada, T.: Sum of Squares Decomposition for Categorical Data. Kwansei Gakuin Studies in Computer Science 14 (1999) 1–6. http://www.media.kwansei.ac.jp/home/kiyou/kiyou99/kiyou99-e.html

    Google Scholar 

  9. Gini, C.W.: Variability and Mutability, contribution to the study of statistical distributions and relations, Studi Economico-Giuridici della R. Universita de Cagliari (1912). Reviewed in Light, R.J., Margolin, B.H.: An Analysis of Variance for Categorical Data. J. Amer. Stat. Assoc. 66(1971) 534–544

    Google Scholar 

  10. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. Proc. VLDB (1994) 487–499

    Google Scholar 

  11. Toivonen, H.: Sampling Large Databases for Finding Association Rules. Proc. VLDB (1996) 134–145

    Google Scholar 

  12. Brin, S., Motwani, R., Ullman J. D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. Proc. ACM SIGMOD (1997) 255–264

    Google Scholar 

  13. Mertz, C. J., Murphy, P. M.: UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html, University of California, Irvine, Dept. of Information and Computer Sci. (1996)

Download references

Author information

Authors and Affiliations

  1. Center for Information & Media Studies, Kwansei Gakuin University, Uegahara 1-1-155, Nishinomiya, 662-8501, Japan

    Takashi Okada

Authors
  1. Takashi Okada
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Okada, T. (2000). Efficient Detection of Local Interactions in the Cascade Model. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-45571-X_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics