A New Approach for Maximal Frequent Sequential Patterns Mining Over Data Streams

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Provided by: AICIT
Topic: Big Data
Format: PDF
Mining data streams for knowledge discovery has been used in many applications, including network traffic monitoring, web click stream mining, network intrusion detection, and dynamic tracing of financial transactions. In this paper, by analyzing characteristics of date stream, the authors 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. By extending the structures of the bitmap data representation of SPAM, they develop an improved algorithm, called SWSS-Imp, to reduce the time in the process of sequential pattern mining.
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