计算机工程Issue(3)：51-54,4.DOI:10.3969/j.issn.1000-3428.2014.03.010
基于不确定性数据的频繁闭项集挖掘算法
Mining Algorithm of Frequent Closed Itemsets Based on Uncertain Data
章淑云 1张守志1
作者信息
- 1. 复旦大学计算机科学技术学院，上海 200433
- 折叠
摘要
Abstract
For the uncertain data, traditional method of judging whether an itemset is frequent cannot express how close the estimate is, meanwhile frequent itemsets are large and redundant for large datasets. Regarding to the above two disadvantages, this paper proposes a mining algorithm of frequent closed itemsets based on uncertain data called UFCIM to mine frequent closed itemsets from uncertain data according to frequent itemsets mining method from uncertain data, and it is based on level mining algorithm Apriori. It uses probability of confidence to express how close the estimate is, the larger that probability of confidence is, the itemsets are more likely to be frequent. Besides as frequent closed itemsets are compact and lossless representation of frequent itemsets, so it uses compacted frequent closed itemsets to take place of frequent itemsets which are of huge size. Experimental result shows the UFCIM algorithm can mine frequent closed itemsets effectively and quickly. It can reduce redundancy and meanwhile assure the accuracy and completeness of itemsets.关键词
不确定性数据/频繁闭项集/数据挖掘/水平挖掘/置信度概率Key words
uncertain data/frequent closed itemsets/data mining/level mining/probability of confidence分类
信息技术与安全科学引用本文复制引用
章淑云,张守志..基于不确定性数据的频繁闭项集挖掘算法[J].计算机工程,2014,(3):51-54,4.
