An Efficient Algorithm for Finding Frequent Items in a Stream

摘要：

Most of the existing algorithms for mining frequent items over data streams do not emphasis the importance of the more recent data items. We present an efficient algorithm where a fading factor λ is used for computing frequency counts exceeding a user-specified threshold over data streams. Our algorithm λ-Miner can detect e-approximate frequent items of a data stream using O(ε-1) memory space and the processing time for each data item is O(1). Experimental results on several artificial data sets and real data sets show that λ-Miner performs better than λ-LC in terms with precision, memory requirement and time cost.

关键词： data stream data mining frequent items fading factor

作者: Li Tu Ling Chen Shan Zhang

作者单位: Institute of Information Science and Technology, Department of Computer Science Nanjing University o Department of Computer Science, National Key Lab of Novel Software Tech Yangzhou University, Nanjing Department of Computer Science Yangzhou University Yangzhou, China

会议类型: 国际会议

会议名称: Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)

会议地点: 南昌

会议语种:英文

页码: 856-860

在线出版日期: 2009-05-22（万方平台首次上网日期，不代表论文的发表时间）