Incremental mining of sequential patterns over a stream sliding window

Chin-Chuan Ho, Hua-Fu Li, Fang-Fei Kuo, Suh-Yin Lee

研究成果: Chapter同行評審

38 引文 斯高帕斯(Scopus)

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.
原文English
主出版物標題ICDM 2006: SIXTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, WORKSHOPS
發行者IEEE
頁面677-+
ISBN(列印)978-0-7695-2702-4
DOIs
出版狀態Published - 2006
事件6th IEEE International Conference on Data Mining - Hong Kong, China
持續時間: 18 十二月 200622 十二月 2006

Conference

Conference6th IEEE International Conference on Data Mining
國家China
城市Hong Kong
期間18/12/0622/12/06

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