期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2015
卷号：20
期号：1
页码：37-40
DOI：10.14445/22312803/IJCTT-V20P107
出版社：Seventh Sense Research Group
摘要：Mining frequent items from large uncertain database is a crucial issue, according to the accuracy, performance and computational cost, where we need the frequent itemset is ascertained efficiently and accurately with low computational cost and high performance in detecting probabilistic frequent item (PFI), so all of these factors are the required or recommended in a large uncertain database to extract the frequent items efficiently and accurately. In uncertain database the support of an item occurs randomly instead of fixed variable. We will use a model based algorithm and dynamic algorithm for mining and generating candidate itemsets for frequent itemsets in large uncertain data. Our goal is a better performance based on our dataset.
