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An Integrated Approach for Mining Meta-rules

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Abstract

An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of scanning database is only twice. In the first time, a set of 1-frequent itemsets and its projection database are formed at every partition. Then every projected database is scanned to construct a hyper-structure. Through mining the hyper-structure, various rules, for example, global association rules, meta-rules, stable association rules and trend rules etc. can be obtained. Compared with existing algorithms for mining association rule, our approach can mine and obtain more useful rules. Compared with existing algorithms for meta-mining or change mining, our approach has higher efficiency. The experimental results show that our approach is very promising.

The work was supported in part by the fund of the Natural Science Plan from University in Jiangsu Province, China, Number: 04KJB460033.

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Author information

Authors and Affiliations

  1. College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Postal code 210016, Nanjing, China

    Feiyue Ye, Jiandong Wang, Huiping Chen, Tianqiang Huang & Li Tao

  2. Department of Computer Science and Technology, Jiangsu Teachers College of Technology, Postal code 213001, Changzhou, China

    Feiyue Ye & Shiliang Wu

Authors
  1. Feiyue Ye

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  2. Jiandong Wang

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  3. Shiliang Wu

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  4. Huiping Chen

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  5. Tianqiang Huang

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  6. Li Tao

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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

  2. Institute of Media and Information Technology, Chiba University, Japan

    Atsushi Imiya

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© 2005 Springer-Verlag Berlin Heidelberg

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Ye, F., Wang, J., Wu, S., Chen, H., Huang, T., Tao, L. (2005). An Integrated Approach for Mining Meta-rules. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_54

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