Conference Paper

Sequential Pattern Mining in Data Streams Using the Weighted Sliding Window Model.

DOI: 10.1109/ICPADS.2009.64 Conference: IEEE 15th International Conference on Parallel and Distributed Systems, ICPADS 2009, 8-11 December 2009, Shenzhen, China
Source: DBLP

ABSTRACT Mining data streams for knowledge discovery is important to many applications, including Web click stream mining, network intrusion detection, and on-line transaction analysis. In this paper, by analyzing data characteristics, we propose an efficient algorithm SWSS (Sequential pattern mining with the weighted sliding window model in SPAM) to mine frequent sequential patterns based on the weighted sliding windows model. This algorithm provides more space for users to specify which sequences they are more interested in. Extensive experiments show that the proposed algorithm is feasible and efficient for mining all sequential patterns as users specified.

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