標題: A new algorithm for maintaining closed frequent itemsets in data streams by incremental updates
作者: Li, Hua-Fu
Ho, Chin-Chuan
Kuo, Fang-Fei
Lee, Suh-Yin
資訊工程學系
Department of Computer Science
公開日期: 2006
摘要: Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams.
URI: http://hdl.handle.net/11536/17345
ISBN: 978-0-7695-2702-4
期刊: ICDM 2006: Sixth IEEE International Conference on Data Mining, Workshops
起始頁: 672
結束頁: 676
Appears in Collections:Conferences Paper