标题: | DSM-FI: an efficient algorithm for mining frequent itemsets in data streams |
作者: | Li, Hua-Fu Shan, Man-Kwan Lee, Suh-Yin 资讯工程学系 Department of Computer Science |
关键字: | Data mining;Data streams;Frequent itemsets;Single-pass algorithm;Landmark window |
公开日期: | 1-十月-2008 |
摘要: | Online mining of data streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some inherent characteristics. In this paper, we propose a new single-pass algorithm, called DSM-FI (data stream mining for frequent itemsets), for online incremental mining of frequent itemsets over a continuous stream of online transactions. According to the proposed algorithm, each transaction of the stream is projected into a set of sub-transactions, and these sub-transactions are inserted into a new in-memory summary data structure, called SFI-forest (summary frequent itemset forest) for maintaining the set of all frequent itemsets embedded in the transaction data stream generated so far. Finally, the set of all frequent itemsets is determined from the current SFI-forest. Theoretical analysis and experimental studies show that the proposed DSM-FI algorithm uses stable memory, makes only one pass over an online transactional data stream, and outperforms the existing algorithms of one-pass mining of frequent itemsets. |
URI: | http://dx.doi.org/10.1007/s10115-007-0112-4 http://hdl.handle.net/11536/8314 |
ISSN: | 0219-1377 |
DOI: | 10.1007/s10115-007-0112-4 |
期刊: | KNOWLEDGE AND INFORMATION SYSTEMS |
Volume: | 17 |
Issue: | 1 |
起始页: | 79 |
结束页: | 97 |
显示于类别: | Articles |
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