Efficient methods for mining weighted clickstream patterns
Efficient methods for mining weighted clickstream patterns
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DOI：
10.1016/j.eswa.2019.112993
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发表时间：
2020-03-15
影响因子：
8.5
通讯作者：
Tseng, Vincent S.
中科院分区：
文献类型：
--
作者：
Huynh, Huy M.;Nguyen, Loan T. T.;Tseng, Vincent S.
模式挖掘自提出以来一直是一个吸引众多研究者的研究课题。点击流挖掘是序列模式挖掘的一个特殊版本，在互联网时代已经被证明是非常重要的。然而，以往的工作大多只是简单地利用和应用现有的序列模式算法挖掘点击流模式，很少有研究具有广泛应用前景的带权重的点击流。在本文中，我们解决这个问题，提出了一种基于平均权重的点击流模式挖掘的方法，并适应以前的国家的最先进的算法来处理加权点击流模式挖掘的问题。在此基础上，我们提出了一种名为Compact-SPADE的改进方法，以提高效率和内存消耗。通过对现实生活和合成数据库的各种测试，我们表明我们提出的算法在效率，内存需求和可扩展性方面优于最先进的替代品。(C)2019爱思唯尔有限公司版权所有。
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.