Reduction based symbolic value partition

  • Authors:

  • Fan Min;Qihe Liu;Chunlan Fang;Jianzhong Zhang

  • Affiliations:

  • School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

  • Venue:
  • ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
  • Year:
  • 2006

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Abstract

Theory of Rough Sets provides good foundations for the attribute reduction processes in data mining. For numeric attributes, it is enriched with appropriately designed discretization methods. However, not much has been done for symbolic attributes with large numbers of values. The paper presents a framework for the symbolic value partition problem, which is more general than the attribute reduction, and more complicated than the discretization problems.We demonstrate that such problem can be converted into a series of the attribute reduction phases. We propose an algorithm searching for a (sub)optimal attribute reduct coupled with attribute value domains partitions. Experimental results show that the algorithm can help in computing smaller rule sets with better coverage, comparing to the standard attribute reduction approaches.