Mining probabilistic datasets vertically

As frequent pattern mining plays an important role in various real-life applications, it has been the subject of numerous studies. Most of the studies mine transactional datasets of precise data. However, there are situations in which data are uncertain. Over the few years, Apriori-based, tree-based, and hyperlinked array structure based mining algorithms have been proposed to mine frequent patterns from these probabilistic datasets of uncertain data. These algorithms view the datasets "horizontally" as collections of transactions, and each records a set of items contained in that transaction. In this paper, we consider an alternative representation such that probabilistic datasets of uncertain data can be viewed "vertically" as collections of vectors. The vector for each item indicates which transactions contain that item. We also propose an algorithm called U-VIPER to mine these probabilistic datasets "vertically for frequent patterns.