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[Efficient methods for mining weighted clickstream patterns]

Anh Nguyen (Katedra Systemów Informatycznych, PWr, Wrocław)


Pattern mining has been an attractive topic for many researchers since its first 
introduction. Clickstream mining, a specific version of sequential pattern mining, has 
been shown to be important in the age of the Internet. However, most previous works 
have simply exploited and applied existing sequential pattern algorithms to the mining 
of clickstream patterns, and few have studied clickstreams with weights, which also 
have a wide range of application. During the presentation, we address this problem by 
proposing an approach based on the average weight measure for clickstream pattern 
mining and adapting a previous state-of-the-art algorithm to deal with the problem of 
weighted clickstream pattern mining. Following this, we propose an improved method 
named Compact-SPADE to enhance both the efficiency and memory consumption. Through 
various tests on both real-life and synthetic databases, we show that our proposed 
algorithms outperform state-of-the-art alternatives in terms of efficiency, memory 
requirements and scalability