Abstract The existing rough set based attribute reduction algorithms are mainly designed for the problem of the underlying data residing in the main memory. Therefore, the limitation of their application to attribute reduction computation of huge data results in a relatively poor scalability. Inspired by supervised learning in quest (SLIQ) algorithm, a specific data pre-processing strategy is introduced and a fast attribute reduction algorithm is proposed with time complexity O(｜U｜｜C｜). The experimental results show that the proposed algorithm is of good scalability.
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2009, Vol. 22 
