Inproceedings,
Mining frequent itemsets from probabilistic datasets
EDB 2013, page 137-148. (2013)
Abstract
Frequent itemset mining aims to discover implicit, previously unknown and potentially useful knowledge—in the form of sets of frequently co-occurring items—that are embedded in data. Many algorithms developed in the early days mined frequent itemsets from traditional transaction databases of precise data such as shoppers' market basket data, in which the contents of databases are known. However, we are living in an uncertain world, in which uncertain data can be found in many real-life applications. Hence, in recent years, researchers have paid more attention to frequent itemset mining from probabilistic datasets of uncertain data. In this paper, we present some algorithms for mining frequent itemsets from these probabilistic datasets.
