Distributed Mining of Constrained Patterns from Wireless Sensor Data

With the advance in technology, wireless sensor networks have been widely used in many application areas such as agricultural and environmental monitoring. 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 intension via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. In this paper, we propose an efficient tree-based system for mining patterns that satisfy user-defined constraints from a distributed environment such as a wireless sensor network. Experimental results show effectiveness of our proposed system.