Efficient single-pass frequent itemsets mining over data streams
Jun Tan, Yingyong Bu, Haiming Zhao · 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery · 2010
Finding frequent itemsets is one of the most important issues in mining data streams for many applications such as web click stream mining, sensor networks, and network traffic analysis. Most prominent algorithms for traditional transaction databases need multiple scans, therefore, they are not suitable for data streams which are continuous, unbounded, usually come with high speed. In this paper, we propose a new single -pass algorithms which use the FP-tree data structure in combination with the IT-matrix technique which greatly reduces the need to traverse FP-trees. The experiment results on synthetic datasets and real datasets show that our proposed algorithm is an efficient method for mining frequent itemsets over data streams.