Distributed Mining of Constrained Frequent Sets from Uncertain Data

With the advance in technology, sensor networks have been widely used in many application areas such as environmental surveillance. Sensors distributed in these networks serve as good sources for data. This calls for distributed data mining, which searches for implicit, previously unknown, and potentially useful patterns that might be embedded in the distributed data. 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, the 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 sets that satisfy user-defined constraints from a distributed environment such as a wireless sensor network of uncertain data. Experimental results show effectiveness of our proposed system.