Finding the most evident co-clusters on web log dataset using frequent super-sequence mining

Xinran Yu, T. Korkmaz
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引用次数: 4

Abstract

It is important to mine the weblog dataset to find interesting and helpful information. There are three kinds of mining on weblog data which are web usage mining, web structure mining and web content mining. In our research, we are going to investigate web pages structure and find the most evident groups of users and web pages. Nowadays, big data is everywhere. Facing huge amount of web logs, it is not always necessary to group all the users in a web log dataset into different clusters, sometimes, finding out the major dominant user groups and the corresponding web pages is more important. In this paper, we are going to investigate a new way to search the most evident co-clusters of users and the corresponding web pages in the web log dataset using frequent super-sequence mining technique. Through experiments we find interesting results.
利用频繁超序列挖掘在web日志数据集上寻找最明显的共聚类
挖掘weblog数据集以找到有趣和有用的信息是很重要的。对博客数据的挖掘有三种类型,即web使用方式挖掘、web结构挖掘和web内容挖掘。在我们的研究中,我们将调查网页结构,并找到最明显的用户和网页组。如今,大数据无处不在。面对海量的web日志,并不总是需要将web日志数据集中的所有用户分组到不同的集群中,有时找出主要的主导用户组和对应的网页更为重要。在本文中,我们将研究一种利用频繁超序列挖掘技术在web日志数据集中搜索最明显的用户共聚类和相应的网页的新方法。通过实验,我们发现了有趣的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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