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A Condensed Representation of Itemsets for Analyzing Their Evolution over Time

  • Mirko Boettcher
  • Martin Spott
  • Rudolf Kruse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)

Abstract

Driven by the need to understand change within domains there is emerging research on methods which aim at analyzing how patterns and in particular itemsets evolve over time. In practice, however, these methods suffer from the problem that many of the observed changes in itemsets are temporally redundant in the sense that they are the side-effect of changes in other itemsets, hence making the identification of the fundamental changes difficult. As a solution we propose temporally closed itemsets, a novel approach for a condensed representation of itemsets which is based on removing temporal redundancies. We investigate how our approach relates to the well-known concept of closed itemsets if the latter would be directly generalized to account for the temporal dimension. Our experiments support the theoretical results by showing that the set of temporally closed itemsets is significantly smaller than the set of closed itemsets.

Keywords

Association Rule Frequent Itemsets Condensed Representation Temporal Redundancy Support History 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Böttcher, M., Spiliopoulou, M., Höppner, F.: On exploiting the power of time in data mining. SIGKDD Explorations Newsletter 10(2), 3–11 (2008)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Psaila, G.: Active data mining. In: Fayyad, U.M., Uthurusamy, R. (eds.) Proceedings of the 1st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Montreal, Quebec, Canada, pp. 3–8. AAAI Press, Menlo Park (1995)Google Scholar
  3. 3.
    Chakrabarti, S., Sarawagi, S., Dom, B.: Mining surprising patterns using temporal description length. In: Proceedings of the 24th International Conference on Very Large Databases, pp. 606–617. Morgan Kaufmann Publishers Inc., San Francisco (1998)Google Scholar
  4. 4.
    Liu, B., Ma, Y., Lee, R.: Analyzing the interestingness of association rules from the temporal dimension. In: Proceedings of the IEEE International Conference on Data Mining, pp. 377–384. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  5. 5.
    Spiliopoulou, M., Baron, S., Günther, O.: Efficient monitoring of patterns in data mining environments. In: Kalinichenko, L.A., Manthey, R., Thalheim, B., Wloka, U. (eds.) ADBIS 2003. LNCS, vol. 2798, pp. 253–265. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Böttcher, M., Spott, M., Nauck, D., Kruse, R.: Mining changing customer segments in dynamic markets. Expert Systems with Applications 36(1), 155–164 (2009)CrossRefGoogle Scholar
  7. 7.
    Berger, C.R.: Slippery slopes to apprehension: Rationality and graphical depictions of increasingly threatening trends. Communication Research 32(1), 3–28 (2005)CrossRefGoogle Scholar
  8. 8.
    Liu, B., Hsu, W., Ma, Y.: Discovering the set of fundamental rule changes. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 335–340 (2001)Google Scholar
  9. 9.
    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Information Systems 24(1), 25–46 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Zaki, M.J., Hsiao, C.J.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Transactions on Knowledge and Data Engineering 17(4), 462–478 (2005)CrossRefGoogle Scholar
  11. 11.
    Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhai, L.: Mining frequent patterns with counting inference. SIGKDD Explorations Newsletter 2(2), 66–75 (2000)CrossRefGoogle Scholar
  12. 12.
    Calders, T., Goethals, B.: Mining all non-derivable frequent itemsets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 74–85. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Catch the moment: maintaining closed frequent itemsets over a data stream sliding window. Knowledge and Information Systems 10(3), 265–294 (2006)CrossRefGoogle Scholar
  14. 14.
    Ruggles, S., Sobek, M., Alexander, T., Fitch, C.A., Goeken, R., Hall, P.K., King, M., Ronnander, C.: Integrated public use microdata series: Version 4.0, machine-readable database. Minnesota population center, Minneapolis (producer and distributor) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mirko Boettcher
    • 1
  • Martin Spott
    • 2
  • Rudolf Kruse
    • 1
  1. 1.Faculty of Computer ScienceUniversity of MagdeburgMagdeburgGermany
  2. 2.Intelligent Systems Research Centre, BT Group plcIpswichUnited Kingdom

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