Skip to main content
SpringerLink
Log in

Just enough learning (of association rules): the TAR2 “Treatment” learner

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

An over-zealous machine learner can automatically generate large, intricate, theories which can be hard to understand. However, such intricate learning is not necessary in domains that lack complex relationships. A much simpler learner can suffice in domains with narrow funnels; i.e. where most domain variables are controlled by a very small subset. Such a learner is TAR2: a weighted-class minimal contrast-set association rule learner that utilizes confidence-based pruning, but not support-based pruning. TAR2 learns treatments; i.e. constraints that can change an agent’s environment. Treatments take two forms. Controller treatments hold the smallest number of conjunctions that most improve the current state of the system. Monitor treatments hold the smallest number of conjunctions that best detect future faulty system behavior. Such treatments tell an agent what to do (apply the controller) and what to watch for (the monitor conditions) within the current environment. Because TAR2 generates very small theories, our experience has been that users prefer its tiny treatments. The success of such a simple learner suggests that many domains lack complex relationships.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abts C, Clark B, Devnani-Chulani S, Horowitz E, Madachy R, Reifer D, Selby R, Steece B (1998) COCOMO II model definition manual. Tech. rep., Center for software engineering, USC, http://sunset.usc.edu/COCOMOII/cocomox.html#downloads

  2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large databases, available from http://www.almaden.ibm.com/cs/people/ragrawal/papers/vldb94_rj.ps

  3. Bay S, Pazzani M (1999) Detecting change in categorical data: mining contrast sets. In: Proceedings of the fifth international conference on knowledge discovery and data mining, available from http://www.ics.uci.edu/~pazzani/Publications/stucco.pdf

  4. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Tech. rep., Wadsworth International, Monterey, CA

  5. Cai C, Fu A, Cheng C, Kwong W (1998) Mining association rules with weighted items. In: Proceedings of international database engineering and applications symposium (IDEAS 98), available from http://www.cse.cuhk.edu.hk/~kdd/assoc_rule/paper.pdf

  6. Clancy D, Kuipers B (1997) Model decomposition and simulation: a component based qualitative simulation algorithm. In: Proceedings of the 14th National Conference on Artificial Intelligence (AAAI-97), AAAI/MIT Press, 1997

  7. Crawford J, Baker A (1994) Experimental results on the application of satisfiability algorithms to scheduling problems. In: AAAI, vol 2. pp 1092–1097

  8. DeKleer J (1986) An assumption-based TMS. Artif Intell 28: 163–196

    Article  Google Scholar 

  9. Feather M, Menzies T (2002) Converging on the optimal attainment of requirements. In: IEEE joint conference on requirements engineering ICRE’02 and RE’02, 9–13th September, University of Essen, Germany, available from http://menzies.us/pdf/02re02.pdf

  10. Feather M, Cornford S, Larson T (2000) Combining the best attributes of qualitative and quantitative risk management tool support. In: 15th IEEE international conference on automated software engineering, Grenoble, France, pp 309–312

  11. Holte R (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11:

    Article  MATH  Google Scholar 

  12. Hu Y (2003) Treatment learning: implementation and application. Masters Thesis, Department of Electrical Engineering, University of British Columbia

  13. Josephson J, Chandrasekaran B, Carroll M, Iyer N, Wasacz B, Rizzoni G (1998) Exploration of large design spaces: an architecture and preliminary results. In: AAAI ’98, available from http://www.cis.ohio-state.edu/~jj/Explore.ps

  14. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324, URL citeseer.nj.nec.com/kohavi96wrappers.html

    Google Scholar 

  15. Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: KDD, pp 80–86, available from http://citeseer.nj.nec.com/liu98integrating.html

  16. Lutz R, Woodhouse R (1999) Bi-directional analysis for certification of safety-critical software. In: 1st International software assurance certification conference (ISACC’99), available from http://www.cs.iastate.edu/~rlutz/publications/isacc99.ps

  17. Madachy R (1997) Heuristic risk assessment using cost factors. IEEE Software 14(3): 51–

    Article  Google Scholar 

  18. Menzies T, Compton P (1997) Applications of abduction: hypothesis testing of neuroendocrinological qualitative compartmental models. Artif Intell Med 10:145–175, available from http://menzies.us/pdf/96aim.pdf

  19. Menzies T, Cukic B (2000a) Adequacy of limited testing for knowledge based systems. International Journal on Artificial Intelligence Tools (IJAIT) Available from http://menzies.us/pdf/00ijait.pdf

  20. Menzies T, Cukic B (2000b) When to test less. IEEE Software 17(5):107–112, available from http://menzies.us/pdf/00iesoft.pdf

    Google Scholar 

  21. Menzies T, Hu Y (2001a) Constraining discussions in requirements engineering. In: First international workshop on model-based requirements engineering, available from http://menzies.us/pdf/01lesstalk.pdf

  22. Menzies T, Hu Y (2001b) Reusing models for requirements engineering. In: First international workshop on model-based requirements engineering, available from http://menzies.us/pdf/01reusere.pdf

  23. Menzies T, Hu Y (2002) Agents in a wild world. In: Rouff C (ed) Formal approaches to agent-based systems, book chapter, available from http://menzies.us/pdf/01agents.pdf

  24. Menzies T, Hu Y (2003) Data mining for very busy people. In: IEEE Computer, available from http://menzies.us/pdf/03tar2.pdf

  25. Menzies T, Singh H (2001) Many maybes mean (mostly) the same thing. In: 2nd International workshop on soft computing applied to software engineering (Netherlands), February, available from http://menzies.us/pdf/00maybe.pdf

  26. Menzies T, Singh H (2003) Many maybes mean (mostly) the same thing. In: Madravio M (ed) Soft computing in software engineering, Springer-Verlag, available from http://menzies.us/pdf/03maybe.pdf

  27. Menzies T, Sinsel E (2000) Practical large scale what-if queries: Case studies with software risk assessment. In: Proceedings ASE 2000, available from http://menzies.us/pdf/00ase.pdf

  28. Menzies T, Easterbrook S, Nuseibeh B, Waugh S (1999) An empirical investigation of multiple viewpoint reasoning in requirements engineering. In: RE ’99, available from http://menzies.us/pdf/99re.pdf

  29. Menzies T, Cukic B, Singh H, Powell J (2000) Testing non determinate systems. In: ISSRE 2000, available from http://menzies.us/pdf/00issre.pdf

  30. Parkes A (1999) Lifted search engines for satisfiability. citeseer.nj.nec.com/parkes99lifted.html

  31. Quinlan R (1992) C4.5: Programs for machine learning. Morgan Kaufman, ISBN: 1558602380

  32. Rymon R (1993) An SE-tree based characterization of the induction problem. In: International conference on machine learning, pp 268–275

  33. Rymon R (1994) An se-tree-based prime implicant generation algorithm. In: Annals of Math and A. I., special issue on Model-Based Diagnosis, vol 11, available from http://citeseer.nj.nec.com/193704.html

  34. Singer J, Gent IP, Smaill A (2000) Backbone fragility and the local search cost peak. J Artif Intell Res 12:235–270, URL citeseer.nj.nec.com/singer00backbone.html

    Google Scholar 

  35. Wang K, He Y, Cheung D, Chin F (2001) Mining confident rules without support requirement. In: 10th ACM International conference on information and knowledge management (CIKM 2001), Atlanta, available from http://www.cs.sfu.ca/~wangk/publications.html

  36. Webb G (2000) Efficient search for association rules. In: Proceeding of KDD-2000 Boston, MA, available from http://citeseer.nj.nec.com/webb00efficient.html

Download references

Author information

Authors and Affiliations

  1. Lane Department of Computer Science, West Virginia University, Morgantown, WV, USA

    Tim Menzies

  2. Department of Electrical & Computer Engineering, University of British Columbia, Vancouver, BC, Canada

    Ying Hu

Authors
  1. Tim Menzies
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tim Menzies.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Menzies, T., Hu, Y. Just enough learning (of association rules): the TAR2 “Treatment” learner. Artif Intell Rev 25, 211–229 (2006). https://doi.org/10.1007/s10462-007-9055-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-007-9055-0

Keywords

Search

Navigation