Exploring Time Series of Patterns: Guided Drill-Down in Hierarchies Using Change Mining on Frequent Item Sets

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 445)

Abstract

In the past years pattern detection has gained in importance for many companies. As the volume of collected data increases so does typically the number of found patterns. To cope with this problem different interestingness measures for patterns have been proposed. Unfortunately, their usefulness turns out to be limited in practical applications. To address this problem, we propose a technique for a guided, visual exploration of patterns rather than presenting analysts with static ordered lists of patterns. Specifically, we focus on a method to guide drill-downs into hierarchical attributes, where we make use of change mining on frequent item sets for pattern discovery.

Keywords

Association Rule Temporal Homogeneity Data Warehouse Aggregation Operator Weight 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agarwal, D., Barman, D., Gunopulos, D., Young, N.E., Korn, F., Srivastava, D.: Efficient and effective explanation of change in hierarchical summaries. In: KDD 2007: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 6–15. ACM, New York (2007), http://doi.acm.org/10.1145/1281192.1281197 CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM, Washington D.C. (1993)Google Scholar
  3. 3.
    Bostock, M.: Sunburst, visualisation example for d3. (2012), http://mbostock.github.com/d3/ex/sunburst.html
  4. 4.
    Böttcher, M.: Contrast and change mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(3), 215–230 (2011), doi:10.1002/widm.27CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Böttcher, M., Spott, M., Kruse, R.: A Condensed Representation of Itemsets for Analyzing Their Evolution over Time. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 163–178. Springer, Heidelberg (2009), http://dx.doi.org/10.1007/978-3-642-04180-8_28 CrossRefGoogle Scholar
  7. 7.
    Böttcher, M., Spott, M., Nauck, D., Kruse, R.: Mining changing customer segments in dynamic markets. Expert Systems with Applications 36(1), 155–164 (2009), http://dx.doi.org/10.1016/j.eswa.2007.09.006 CrossRefGoogle Scholar
  8. 8.
    Kimball, R.: Data Warehouse Toolkit: Practical Techniques for Building High Dimensional Data Warehouses. John Wiley & Sons (1996)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)CrossRefGoogle Scholar
  10. 10.
    Pei, J., Han, J., Lakshmanan, L.V.S.: Pushing convertible constraints in frequent itemset mining. Data Mining and Knowledge Discovery 8(3), 227–252 (2004), http://dx.doi.org/10.1023/B:DAMI.0000023674.74932.4c MathSciNetCrossRefGoogle Scholar
  11. 11.
    Sarawagi, S.: Explaining differences in multidimensional aggregates. In: VLDB 1999: Proceedings of the 25th International Conference on Very Large Data Bases, pp. 42–53. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  12. 12.
    Schmidt, F., Spott, M.: Visualising temporal item sets – guided drill-down with hierarchical attributes. In: Proceedings of Soft Methods in Probability and Statistics, SMPS 2012 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversity of MagdeburgMagdeburgGermany
  2. 2.Research and TechnologyBTIpswichUK

Personalised recommendations