Frequent itemset mining of distributed uncertain data under user-defined constraints

Many existing distributed data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. Moreover, due to inherited measurement inaccuracies and/or network latencies, data are often riddled with uncertainty. These call for constrained mining and uncertain data mining. In this paper, we propose a tree-based system for mining frequent itemsets that satisfy user-defined constraints from a distributed environment such as a wireless sensor network of uncertain data. Copyright (c) 2012 - Edizioni Libreria Progetto and the authors.