標題: Efficient methods for mining weighted clickstream patterns
作者: Huynh, Huy M.
Nguyen, Loan T. T.
Vo, Bay
Anh Nguyen
Tseng, Vincent S.
資訊工程學系
Department of Computer Science
關鍵字: Data mining;Weighted clickstream pattern mining;Sequential pattern mining
公開日期: 15-Mar-2020
摘要: 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. In this paper, 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. (C) 2019 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2019.112993
http://hdl.handle.net/11536/153416
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2019.112993
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 142
起始頁: 0
結束頁: 0
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