关键词: 抽样 两阶段 频繁项目集 剪枝 精度

Abstract:

Traditional two-phase sampling algorithms extract the sample data used on data mining from a huge data set. The algorithm efficiency is lower when the  data set is oversized, and the sample accuracy is lower when the data set is an oversized sparse one. By improving the traditional two-phase sampling a   lgorithms, the paper presents a new sampling algorithm named EAFAST, which adjusts algorithm parameters adaptively and performs heuristic search using t  he historical information fully. Experiments demonstrate EAFAST can enhance the efficiency and sample accuracy simultaneously,and thus remedies the insu fficiencies of traditional algorithms.

Key words:  （sample, two-phase, frequent item set, trim, accuracy）