Abstract
In general, cases capture knowledge and concrete experiences of specific situations. By exploiting case-based knowledge for characterizing a subgroup pattern, additional information about the subgroup objects can be provided. This paper proposes a case-based approach for characterizing and analyzing subgroup patterns: It presents techniques for retrieving characteristic factors and a set of corresponding cases for the inspection and analysis of a specific subgroup pattern. Then, the set of factors and cases are merged into prototypical cases for presentation to the user. Such an alternative view on the subgroup pattern provides important introspective information on the subgroup objects, that is, the cases covered by the subgroup description: Using drill-down techniques, the user can perform a detailed introspection of a subgroup pattern using prototypical pattern cases. Additionally, these enable a convenient retrieval of interesting (meta-)information associated with the respective subgroup objects.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59
Aha DW (1992) Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. Int J Man–Mach Stud 36(2):267–287
Atzmueller M, Baumeister J, Hemsing A, Richter E-J, Puppe F (2005) Subgroup mining for interactive knowledge refinement. In: Proceedings of the 10th conference on artificial intelligence in medicine (AIME 05). Lecture notes in artificial intelligence, vol 3581. Springer, Berlin, pp 453–462
Atzmueller M, Baumeister J, Puppe F (2006) Introspective subgroup analysis for interactive knowledge refinement. In: Sutcliffe G, Goebel R (eds) Proceedings of the 19th international Florida artificial intelligence research society conference 2006 (FLAIRS-2006). AAAI, Menlo Park, pp 402–407
Atzmueller M, Baumeister J, Puppe F (2006) Semi-automatic learning of simple diagnostic scores utilizing complexity measures. Artif Intell Med 37(1):19–30, Special issue on intelligent data analysis in medicine
Atzmueller M, Puppe F, Buscher H-P (2005) Exploiting background knowledge for knowledge-intensive subgroup discovery. In: Proceedings of the 19th international joint conference on artificial intelligence (IJCAI-05), Edinburgh, Scotland, pp 647–652
Atzmueller M, Puppe F, Buscher H-P (2005) Profiling examiners using intelligent subgroup mining. In: Proceedings of the 10th international workshop on intelligent data analysis in medicine and pharmacology (IDAMAP-2005), Aberdeen, Scotland, pp 46–51
Bareiss R (1989) Exemplar-based knowledge acquisition: a unified approach to concept representation, classification, and learning. Academic, San Diego
Bartsch-Spörl B, Lenz M, Hübner A (1999) Case-based reasoning: survey and future directions. In: XPS-99: knowledge-based systems—survey and future directions, proceedings of the 5th biannual German conference on knowledge-based systems, pp 67–89
Bergmann R (2002) Experience management: foundations, development methodology, and Internet-based applications. Springer, Berlin
Gamberger D, Krstacic A, Krstacic G, Lavrac N, Sebag M (2005) Data analysis based on subgroup discovery: experiments in brain ischaemia domain. In: Proceedings of the 10th international workshop on intelligent data analysis in medicine and pharmacology (IDAMAP-2005), Aberdeen, Scotland, pp 52–56
Gamberger D, Lavrac N, Krstacic G (2003) Active subgroup mining: a case study in coronary heart disease risk group detection. Artif Intell Med 28:27–57
Hall MA (2000) Correlation-based feature selection for discrete and numeric class machine learning: In: Proceedings of the 17th international conference on machine learning. Kaufmann, San Francisco, pp 359–366
Huettig M, Buscher G, Menzel T, Scheppach W, Puppe F, Buscher H-P (2004) A diagnostic expert system for structured reports, quality assessment, and training of residents in sonography. Med Klin 99(3):117–122
Klösgen W (1996) Explora: A multipattern and multistrategy discovery assistant. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining, AAAI, Menlo Park, pp 249–271
Lavrac N, Gamberger D, Flach P (2002) Subgroup discovery for actionable knowledge generation: shortcomings of classification rule learning and the lessons learned. In: Lavrac N, Motoda H, Fawcett T (eds) Proceedings of the ICML 2002 workshop on data mining: lessons learned, July 2002
McSherry D (2002) Diversity-conscious retrieval. In: Proceedings 6th European conference on advances in case-based reasoning. Springer, Berlin, pp 219–233
Puppe F (1998) Knowledge reuse among diagnostic problem-solving methods in the shell-kit D3. Int J Human–Comput Stud 49:627–649
Schmidt R, Gierl L (2001) Case-based reasoning for antibiotics therapy advice: an investigation of retrieval algorithms and prototypes. Artif Intell Med 23(2):171–186
Smyth B, McClave P (2001) Similarity vs. diversity. In: Proceedings of the 4th International conference on case-based reasoning (ICCBR 01). Springer, Berlin, pp 347–361
Witten IH, Frank E (1999) Data mining: practical machine learning tools and techniques with Java implementations. Kaufmann, Los Altos
Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In: Komorowski J, Zytkow J (eds) Proceedings of the 1st European symposium on principles of data mining and knowledge discovery (PKDD-97). Springer, Berlin, pp 78–87
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Atzmueller, M., Puppe, F. A case-based approach for characterization and analysis of subgroup patterns. Appl Intell 28, 210–221 (2008). https://doi.org/10.1007/s10489-007-0057-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-007-0057-z