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Modeling and Mining the Rule Evolution

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Advanced Data Mining and Applications (ADMA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4093))

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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.

Supported by the National Natural Science Foundation of China under Grant No. 70372024.

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Author information

Authors and Affiliations

  1. Management School, Jinan University, Guangzhou, 510632, China

    Ding Pan

  2. Department of Computer Science, Xi’an Jiaotong University, Xi’an, 710049, China

    Ding Pan

Authors
  1. Ding Pan
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electronic Engineering, The University of Queensland, Queensland, Australia

    Xue Li

  2. University of Alberta, Canada

    Osmar R. Zaïane

  3. Northwest Polytechnical University, China

    Zhanhuai Li

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© 2006 Springer-Verlag Berlin Heidelberg

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Pan, D. (2006). Modeling and Mining the Rule Evolution. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_68

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  • DOI: https://doi.org/10.1007/11811305_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37025-3

  • Online ISBN: 978-3-540-37026-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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