Efficient Mining of Constrained Frequent Patterns from Streams

With advances in technology, a flood of data can be produced in many applications such as sensor networks and Web click streams. This calls for stream mining, which searches for implicit, previously unknown, and potentially useful information (such as frequent patterns) that might be embedded in continuous data streams. However, most of the existing algorithms do not allow users to express the patterns to be mined according to their intentions, via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. In this paper, we develop algorithms - which use a tree-based framework to capture the important portion of the streaming data, and allow human users to impose a certain focus on the mining process - for mining frequent patterns that satisfy user constraints from the flood of data