摘要
现有基于粗糙集的属性约简算法主要针对数据全部驻留在内存中的情况,因此算法不适合海量数据的约简计算、可伸缩性较差.本文借助SLIQ算法的思想并引入相应的一种数据预处理策略,由此提出一个快速的属性约简算法,其时间复杂性为O(|U||C|).实验结果表明该算法具有良好的可伸缩性.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第2期234-239,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60805042)
福建省自然科学基金(No.A0710006)资助项目
关键词
粗糙集
属性约简
海量数据
可伸缩性
Rough Set, Attribute Reduction, Huge Data, Scalability
