標題: Incremental mining of sequential patterns over a stream sliding window
作者: Ho, Chin-Chuan
Li, Hua-Fu
Kuo, Fang-Fei
Lee, Suh-Yin
資訊工程學系
Department of Computer Science
公開日期: 2006
摘要: Incremental mining of sequential patterns from data streams is one of the most challenging problems in mining data streams. However, previous work of mining sequential patterns from data streams is almost focused on mining of patterns from stream of item-sequences, not stream of itemset-sequences. In this paper, we propose an efficient single-pass algorithm, called IncSPAM, to maintain the set of sequential patterns from itemset-sequence 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 IncSPAM algorithm is efficient for mining sequential patterns over data streams.
URI: http://hdl.handle.net/11536/17112
ISBN: 978-0-7695-2702-4
期刊: ICDM 2006: Sixth IEEE International Conference on Data Mining, Workshops
起始頁: 677
結束頁: 681
顯示於類別:會議論文