In order to understand web-based application user behavior, web usage mining applies unsupervised learning techniques to discover hidden patterns from web data that captures user browsing on web sites. For this purpose, web session clustering has been among the most popular approaches to group users with similar browsing patterns that reflect their common interest. An adequate web session clustering implementation significantly depends on the measure that is used to evaluate the similarity of sessions. An efficient approach to evaluate session similarity is sequence alignment, which is known as the task of determining the similarity of elements between sequences. In this paper, we review and compare sequence alignment-based measures for web sessions, and also discuss sequence similarity measures that are not alignment-based. This review also provides a perspective of sequence similarity measures that manipulate web sessions in usage clustering process.

中文翻译：

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基于对齐的 Web 使用挖掘相似性度量综述

为了了解基于 Web 的应用程序用户行为，Web 使用挖掘应用无监督学习技术从捕获用户浏览 Web 站点的 Web 数据中发现隐藏模式。为此，网络会话聚类一直是最流行的方法之一，用于将具有相似浏览模式并反映他们共同兴趣的用户分组。一个适当的网络会话聚类实现很大程度上取决于用于评估会话相似性的度量。评估会话相似性的一种有效方法是序列比对，它被称为确定序列之间元素相似性的任务。在本文中，我们回顾和比较了基于序列比对的 Web 会话度量，并讨论了不基于比对的序列相似性度量。