Frequent Patterns Mining over Data Stream Using an Efficient Tree Structure

Mining frequent patterns over data streams is an interesting problem due to its wide application area. In this study, a novel method for sliding window frequent patterns mining over data streams is proposed. This method utilizes a compressed and memory efficient tree data structure to store and to maintain sliding window transactions. The method dynamically reconstructs and compresses tree data structure to control the amount of memory usage. Moreover, the mining task is efficiently performed using the data structure when a user issues a mining request. The mining process reuses the tree structure to extract frequent patterns and does not need additional memory requirement. Experimental evaluations on real datasets show that our proposed method outperforms recently proposed sliding window based algorithms.