使用神经模糊聚类算法的Web个性化

K. Menon, C. Dagli
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引用次数: 7

摘要

不同的用户对同一个网页有不同的需求,因此有必要开发一个了解用户需求的系统。Web服务器日志包含大量关于访问它的用户的性质的信息。在本文中,我们讨论了如何使用无监督和竞争学习算法(如Kohonen的自组织地图(SOM))来挖掘给定时间段内的这些web服务器日志,并使用统一距离矩阵(U-matrix)来解释这些结果。这些算法帮助我们有效地基于相似的web访问模式对用户进行聚类,每个聚类都有具有相似浏览模式的用户。这些集群在网络个性化中很有用,这样它就可以更好地与用户沟通,也可以在网络流量分析中预测给定时间段的网络流量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Web personalization using neuro-fuzzy clustering algorithms
Different users have different needs from the same web page and hence it is necessary to develop a system which understands the needs and demands of the users. Web server logs have abundant information about the nature of users accessing it. In this paper we discussed how to mine these web server logs for a given period of time using unsupervised and competitive learning algorithm like Kohonen's self organizing maps (SOM) and interpreting those results using Unified distance Matrix (U-matrix). These algorithms help us in efficiently clustering users based on similar web access patterns and each cluster having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users and also in web traffic analysis for predicting web traffic at a given period of time.
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