Mining compressed frequent itemsets over data stream in sliding windows
Recent studies have shown mining compressec frequent itemset patterns provides more benefits than mining the closed frequent patterns, since mining compressed frequent itemset patterns leads to more compact and representative result sets. Especially, it is quite meaningful in the environment of datsa stream where limited memory space and computation quality are major challenges. In this paper, the problem of mining compressed frequent itemset patterns over a data stream sliding windows is presented and studied. Firstly, a novel data structure CP-Tree (Compressed Pattern Tree) is designed to maintain a dynamically selected set of compressed frequent itemset patterns over sliding window. Secondly, an efficient algorithm CFPStream (Compressing Frequent Patterns over Stream) is developed to discover compressed frequent itemset patterns in data stream sliding windows incrementally. Finally, some optimization techniques are adopted in CFPStream to speed up the algorithm and prune search space. Experiments on both real and synthetie data sets show that CFPStream outperforms representative algorithms for the state-of-the-art approaches.
data mining data stream sliding window
Li Zhao Yongxin Tong Dan Yu Shilong Ma Mengdong Chen
State Key Lab.of Software Development Environment Beihang University,Beijing 100191,China
国际会议
上海
英文
713-717
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）