An Efficient Algorithm for Mining Maximal Frequent Patterns over Data Streams

Abstract

For the environment of data stream, an effective algorithm DSM-Miner for mining maximal frequent patterns is proposed. It uses Transactions Sliding Window to specify the number of transactions in each treatment process, and distinguishes and treats the old and new transactions by the way of decaying, meanwhile it takes advantage of the proposed Sliding Window Maximum frequent pattern Tree SWM-Tree to maintain the information of patterns. In the mining process of maximal frequent patterns, the algorithm uses the corresponding node of MFP-Tree as the root of an enumeration tree and uses this enumeration tree as a search space. In addition, the algorithm also adopts appropriate pruning operations, calculation pattern of bit items group and "depth-first" search strategies and ideas. Experimental results show that DSM-Miner algorithm has better space and time performance.