Skip to main content
SpringerLink
Log in

Rule cubes for causal investigations

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

With the complexity of modern vehicles tremendously increasing, quality engineers play a key role within today’s automotive industry. Field data analysis supports corrective actions in development, production and after sales support. We decompose the requirements and show that association rules, being a popular approach to generating explanative models, still exhibit shortcomings. Interactive rule cubes, which have been proposed recently, are a promising alternative. We extend this work by introducing a way of intuitively visualizing and meaningfully ranking them. Moreover, we present methods to interactively factorize a problem and validate hypotheses by ranking patterns based on expectations, and by browsing a cube-based network of related influences. All this is currently in use as an interactive tool for warranty data analysis in the automotive industry. A real-world case study shows how engineers successfully use it in identifying root causes of quality issues.

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. Blumenstock A, Hipp J, Kempe S, Lanquillon C, Wirth R (2006) Interactivity closes the gap. In: Ghani R, Soares C(eds) Data mining for business applications: KDD-2006 workshop. ACM, New York

    Google Scholar 

  2. Borgelt C, Kruse R (2002) Induction of association rules: apriori implementation. In: Klinke S, Ahrend P, Richter L(eds) Computational statistics. Proceedings of the 15th CompStat 2002. Physica-Verlag, Heidelberg

    Google Scholar 

  3. Darroch JN, Ratcliff D (1972) Generalized iterative scaling for log-linear models. Ann Math Stat 43: 1470–1480

    Article  MATH  MathSciNet  Google Scholar 

  4. Elomaa T, Rousu J (1999) General and efficient multisplitting of numerical attributes. Mach Learn 36(3): 201–244

    Article  MATH  Google Scholar 

  5. Elomaa T, Rousu J (2001) On the computational complexity of optimal multisplitting. Fund Informa 47(1–2): 35–52

    MATH  MathSciNet  Google Scholar 

  6. Gondek D, Hofmann T (2007) Non-redundant data clustering. Knowl Inform Syst 12(1): 1–24

    Article  Google Scholar 

  7. Hipp J, Güntzer U, Nakhaeizadeh G (2000) Algorithms for association rule mining—a general survey and comparison. SIGKDD explorations 2(1): 58–64

    Article  Google Scholar 

  8. Hofmann H, Siebes A, Wilhelm A (2000) Visualizing association rules with interactive mosaic plots. In: KDD 00: proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 227–235

  9. Jaroszewicz S, Scheffer T (2005) Fast discovery of unexpected patterns in data, relative to a bayesian network. In: KDD 05: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 118–127

  10. Jaroszewicz S, Simovici D (2002) Pruning redundant association rules using maximum entropy principle. In: PAKDD’02: Proceedings of the sixth Pacific-Asia conference on advances in knowledge discovery and data mining. Taipei, Taiwan, pp 135–147

  11. Knobbe AJ, Ho EKY (2005) Numbers in multi-relational data mining, In: Knowledge discovery in databases: PKDD 2005’. Springer, Heidelberg, pp 544–551

  12. Liu B, Hu M, Hsu W (2000) Multi-level organization and summarization of the discovered rules. In: KDD 00: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York

  13. Padmanabhan B, Tuzhilin A (2000) Small is beautiful: discovering the minimal set of unexpected patterns. In: KDD 00: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 54–63

  14. Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro G, Frawley W(eds) Knowledge discovery in databases. AAAI/MIT Press, Cambridge, pp 229–248

    Google Scholar 

  15. Sahar S (1999) Interestingness via what is not interesting. In: KDD 99: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 332–336

  16. Scholz M (2005) Sampling-based sequential subgroup mining. In: KDD 95: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 265–274

  17. Silberschatz A, Tuzhilin A (1995) On subjective measures of interestingness in knowledge discovery. In: KDD 95: Proceedings of the first ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 275–281

  18. Spirtes P, Glymour C, Scheines R (2001) Causation, Prediction, and Search. MIT Press, Cambridge

    MATH  Google Scholar 

  19. Srikant R, Agrawal R (1997) Mining generalized association rules. Future Generation Computer Systems 13(2–3): 161–180

    Article  Google Scholar 

  20. Steinbach M, Kumar V (2007) Generalizing the notion of confidence. Knowl Inform Syst 12(3): 279–299

    Article  Google Scholar 

  21. Yan X, Cheng H, Han J, Xin D (2005) Summarizing itemset patterns: a profile-based approach. In: KDD 05: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York

  22. Zhao K, Liu B, Benkler J, Xiao W (2006) Opportunity map: Identifying causes of failure—a deployed data mining system. In: KDD 06: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York

Download references

Author information

Authors and Affiliations

  1. Department of Applied Information Processing, University of Ulm, Ulm, Germany

    Axel Blumenstock & Franz Schweiggert

  2. Laboratory for Semantic Information Technology, University of Bamberg, Bamberg, Germany

    Markus Müller

  3. Department of Knowledge and Language Engineering, University of Magdeburg, Magdeburg, Germany

    Carsten Lanquillon

Authors
  1. Axel Blumenstock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Franz Schweiggert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Markus Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carsten Lanquillon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Axel Blumenstock.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Blumenstock, A., Schweiggert, F., Müller, M. et al. Rule cubes for causal investigations. Knowl Inf Syst 18, 109–132 (2009). https://doi.org/10.1007/s10115-008-0141-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-008-0141-7

Keywords

Search

Navigation