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Benchmark of Rule-Based Classifiers in the News Recommendation Task

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Book cover Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9283))

Abstract

In this paper, we present experiments evaluating Association Rule Classification algorithms on on-line and off-line recommender tasks of the CLEF NewsReel 2014 Challenge. The second focus of the experimental evaluation is to investigate possible performance optimizations of the Classification Based on Associations algorithm. Our findings indicate that pruning steps in CBA reduce the number of association rules substantially while not affecting accuracy. Using only part of the data employed for the rule learning phase in the pruning phase may also reduce training time while not affecting accuracy significantly.

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References

  1. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)

    Article  Google Scholar 

  2. Coenen, F., Leng, P., Ahmed, S.: Data structure for association rule mining: T-trees and p-trees. IEEE Transactions on Knowledge and Data Engineering 16(6), 774–778 (2004)

    Article  Google Scholar 

  3. Cohen, W.W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, ML’95, pp. 115–123. Morgan Kaufmann, Lake Tahoe (1995)

    Google Scholar 

  4. Fürnkranz, J.: FOSSIL: a robust relational learner. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 122–137. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  5. Fürnkranz, J., Gamberger, D., Lavrač, N.: Foundations of Rule Learning. Springer (2012)

    Google Scholar 

  6. Hahsler, M., Grün, B., Hornik, K.: Arules - a computational environment for mining association rules and frequent item sets. Journal of Statistical Software 14(15), 1–25 (2005)

    Article  Google Scholar 

  7. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000)

    Article  Google Scholar 

  8. Kille, B., Hopfgartner, F., Brodt, T., Heintz, T.: The plista dataset. In: Proceedings of the International Workshop and Challenge on News Recommender Systems, NRS’13, pp. 14–22. ACM, October 2013

    Google Scholar 

  9. Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) The 2001 IEEE International Conference on Data Mining (ICDM’01), pp. 369–376. IEEE Computer Society (2001)

    Google Scholar 

  10. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD’98), pp. 80–86. AAAI Press, August 1998

    Google Scholar 

  11. Moghimipour, I. Ebrahimpour, M.: Comparing decision tree method over three data mining software. International Journal of Statistics and Probability 3(3) (2014)

    Google Scholar 

  12. Quinlan, J.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)

    Google Scholar 

  13. Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5(3), 239–266 (1990)

    Google Scholar 

  14. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)

    Google Scholar 

  15. Vanhoof, K., Depaire, B.: Structure of association rule classifiers: a review. In: International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 9–12, November 2010

    Google Scholar 

  16. Yin, X., Han, J.: CPAR: classification based on predictive association rules. In: Proceedings of the SIAM International Conference on Data Mining, pp. 369–376. SIAM Press, San Franciso (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Multimedia and Vision Research Group, Queen Mary University of London, London, UK

    Tomáš Kliegr

  2. Web Engineering Group, Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic

    Jaroslav Kuchař

  3. Faculty of Informatics and Statistics, Department of Information and Knowledge Engineering, University of Economics Prague, Prague, Czech Republic

    Tomáš Kliegr & Jaroslav Kuchař

Authors
  1. Tomáš Kliegr
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  2. Jaroslav Kuchař
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Corresponding author

Correspondence to Jaroslav Kuchař .

Editor information

Editors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse, Toulouse , France

    Josanne Mothe

  2. Department of Computer Science, University of Neuchatel, Neuchâtel, Switzerland

    Jacques Savoy

  3. Faculteit der Geesteswetenschappen, Universiteit Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  4. Institut de Recherche en Informatique de Toulouse, Toulouse, France

    Karen Pinel-Sauvagnat

  5. School of Computing, Dublin City University, Dublin, Ireland

    Gareth Jones

  6. LIA - CERI, Université d'Avignon et des Pays de Vaucluse, Avignon, France

    Eric San Juan

  7. Department of Information Engineering, University of Padua, Padua, Italy

    Linda Capellato

  8. of Information Engineering (DEI), University of Padua, Department, Padova, Italy

    Nicola Ferro

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Kliegr, T., Kuchař, J. (2015). Benchmark of Rule-Based Classifiers in the News Recommendation Task. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-24027-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24026-8

  • Online ISBN: 978-3-319-24027-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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