efficient algorithms for stream mining of constrained frequent patterns in a limited memory environment
As technology advances, streams of data can be rapidly generated in many real-life applications. 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. As a result, these unconstrained mining algorithms can yield numerous patterns that are not interesting to the users. Moreover, many existing tree-based algorithms assume that all the trees constructed during the mining process can fit into memory. While this assumption holds for many situations, there are many other situations in which it does not hold. Hence, in this paper, we develop efficient algorithms for stream mining of constrained frequent patterns in a limited memory environment . Our algorithms allow users to impose a certain focus on the mining process, discover from data streams all those frequent patterns that satisfy the user constraints, and handle situations where the available memory space is limited.
data streams constraints limited memory space frequent itemsets data mining