Efficient Frequent Itemsets Mining by Sampling

As the first stage for discovering association rules, frequent itemsets mining is an important challenging task for large databases. Sampling provides an efficient way to get approximating answers in much shorter time. Based on the characteristics of frequent itemsets counting, a new bound for sampling is proposed, with which less samples are necessary to achieve the required accuracy and the efficiency is much improved over traditional Chernoff bounds. Keywords. Frequent itemset, sampling

• Type: Conference
• Year: 2006
• Where: AMT
• Authors: Yanchang Zhao, Chengqi Zhang, Shichao Zhang
• Keywords: AMT 2006, Frequent Itemset, Frequent Itemsets Mining, Important Challenging Task, Multimedia