Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.


Attribute Reduction Decision Table Decision Attribute Conditional Attribute Discernibility Matrix 
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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuguo He
    • 1
  1. 1.Department of Computer Science and EngineeringBeijing Institute of TechnologyBeijingChina

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