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Modeling and Mining the Rule Evolution

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

Abstract

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.

Keywords

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.

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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

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