Distributed Uncertain Data Mining for Frequent Patterns Satisfying Anti-monotonic Constraints
Carson Kai-Sang Leung, Richard Kyle MacKinnon, Fan Jiang · 2014
High volumes of uncertain data can be generated in distributed environments in many real-life biological, medical and life science applications. As an important data mining task, frequent pattern mining helps discover frequently co-occurring items, objects, or events from these distributed databases. However, users may be interested in only some small portions of all the frequent patterns that can be mined from these databases. In this paper, we propose an intelligent computing system that (i) allows users to express their interests via the use of user-specified constraints and (ii)effectively exploits anti-monotonic properties of user-specified constraints and efficiently discovers frequent patterns satisfying these constraints from the distributed databases containing uncertain data.