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Using Declarative Specifications of Domain Knowledge for Descriptive Data Mining

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5437))

Abstract

Domain knowledge is a valuable resource for improving the quality of the results of data mining methods. In this paper, we present a methodological approach for providing domain knowledge in a declarative manner: We utilize a Prolog knowledge base with facts for the specification of properties of ontological concepts and rules for the derivation of further ad-hoc relations between these concepts. This enhances the documentation, extendability, and standardization of the applied knowledge. Furthermore, the presented approach also provides for potential automatic verification and improved maintenance options with respect to the used domain knowledge.

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References

  1. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. 20th Int. Conf. Very Large Data Bases (VLDB 1994), pp. 487–499. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  2. Atzmueller, M., Puppe, F.: A Knowledge-Intensive Approach for Semi-Automatic Causal Subgroup Discovery. In: Proc. Workshop on Prior Conceptual Knowledge in Machine Learning and Knowledge Discovery (PriCKL 2007), at the 18th European Conference on Machine Learning (ECML 2007), 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007), Warsaw, Poland, pp. 1–6 (2007)

    Google Scholar 

  3. Atzmüller, M., Puppe, F.: A methodological view on knowledge-intensive subgroup discovery. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS, vol. 4248, pp. 318–325. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Atzmueller, M., Puppe, F.: SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS, vol. 4213, pp. 6–17. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Atzmueller, M., Puppe, F., Buscher, H.P.: Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery. In: Proc. 19th Intl. Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, pp. 647–652 (2005)

    Google Scholar 

  6. Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  7. Baumeister, J., Atzmueller, M., Puppe, F.: Inductive Learning for Case-Based Diagnosis with Multiple Faults. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS, vol. 2416, pp. 28–42. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Huettig, M., Buscher, G., Menzel, T., Scheppach, W., Puppe, F., Buscher, H.P.: A Diagnostic Expert System for Structured Reports, Quality Assessment, and Training of Residents in Sonography. Medizinische Klinik 99(3), 117–122 (2004)

    Article  Google Scholar 

  9. Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns Without Candidate Generation. In: Chen, W., Naughton, J., Bernstein, P.A. (eds.) Proc. ACM SIGMOD Intl. Conference on Management of Data (SIGMOD 2000), pp. 1–12. ACM Press, New York (2000)

    Chapter  Google Scholar 

  10. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  11. Jaroszewicz, S., Simovici, D.A.: Interestingness of Frequent Itemsets using Bayesian Networks as Background Knowledge. In: Proc. 10th Intl. Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 178–186. ACM Press, New York (2004)

    Chapter  Google Scholar 

  12. Klösgen, W.: 16.3: Subgroup Discovery. In: Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)

    Google Scholar 

  13. Klösgen, W.: Explora: A Multipattern and Multistrategy Discovery Assistant. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI Press, Menlo Park (1996)

    Google Scholar 

  14. Lavrac, N., Kavsek, B., Flach, P., Todorovski, L.: Subgroup Discovery with CN2-SD. Journal of Machine Learning Research 5, 153–188 (2004)

    MathSciNet  Google Scholar 

  15. Richardson, M., Domingos, P.: Learning with Knowledge from Multiple Experts. In: Proc. 20th Intl. Conference on Machine Learning (ICML 2003), pp. 624–631. AAAI Press, Menlo Park (2003)

    Google Scholar 

  16. Seipel, D.: Processing XML-Documents in Prolog. In: Proc. 17th Workshop on Logic Programming (WLP 2002), Dresden (2002)

    Google Scholar 

  17. Wrobel, S.: An Algorithm for Multi-Relational Discovery of Subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  18. Zelezny, F., Lavrac, N., Dzeroski, S.: Using Constraints in Relational Subgroup Discovery. In: Intl. Conference on Methodology and Statistics, Ljubljana, Slovenia, pp. 78–81 (2003)

    Google Scholar 

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Atzmueller, M., Seipel, D. (2009). Using Declarative Specifications of Domain Knowledge for Descriptive Data Mining. In: Seipel, D., Hanus, M., Wolf, A. (eds) Applications of Declarative Programming and Knowledge Management. INAP WLP 2007 2007. Lecture Notes in Computer Science(), vol 5437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00675-3_10

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  • DOI: https://doi.org/10.1007/978-3-642-00675-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00674-6

  • Online ISBN: 978-3-642-00675-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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