Modeling and Mining the Rule Evolution

  • Ding Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Temporal data mining attempts to provide accurate information about an evolving business domain. A framework is proposed to discover continuously temporal knowledge based on a session model. The main concepts and properties in temporal rule induction are defined and proved in a formal way, using first-order linear temporal logic. The measures of first-order rule are used to discover evolutional regularity about the rule. The mining process consists of four stages: planning, session mining, merge mining, and post-processing. Various session mining for temporal data generates a measure sequence of first-order rule. The parameter estimation method applicable to the measure sequence with a small-sample is presented, based on the principle of information diffusion. Experiment shows the validity and simplicity of the method.


Information Diffusion Session Mining Diffusion Estimation Measure Sequence Rule Evolution 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Psaila, G.: Active data mining. In: Proc. of the KDD 1995, pp. 3–8. AAAI Press, California (1995)Google Scholar
  2. 2.
    Roddick, J.F., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. IEEE Trans. on Knowledge and Data Engineering 14(4), 750–768 (2002)CrossRefGoogle Scholar
  3. 3.
    Keogh, E.J., Kasetty, S.: On the Need for Time Series Data Mining Benchmarks. Data Mining and Knowledge Discovery 7(4), 349–371 (2003)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Cotofrei, P., Stoffel, K.: From Temporal Rules to Temporal Meta-rules. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 169–178. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Spiliopoulou, M., Roddick, J.F.: Higher Order Mining. In: Proc. 2nd Intl. Conf. on Data Mining Methods and Databases, Cambridge, UK, pp. 309–320 (2000)Google Scholar
  6. 6.
    Huang, C.F., Shi, Y.: Towards Efficient Fuzzy Information Processing - Using the Principle of Information Diffusion. Physica-Verlag, Heidelberg (2002)CrossRefMATHGoogle Scholar
  7. 7.
    Pan, D., Shen, J.: Ontology Service-based Architecture for Continuous Knowledge Discovery. In: Proc. of the 4th ICMLC 2005, pp. 2155–2160. IEEE Press, Guangzhou (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ding Pan
    • 1
    • 2
  1. 1.Management SchoolJinan UniversityGuangzhouChina
  2. 2.Department of Computer ScienceXi’an Jiaotong UniversityXi’anChina

Personalised recommendations