基于内聚马尔可夫模型和Apriori算法的Web使用模式检测

2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) Pub Date : 2022-06-25 DOI:10.1109/i2cacis54679.2022.9815465

Vinukumar Luckose, Jothish Chembath, Joe Arun Raja Ponnusamy, Sakshi Sharma, Pritpal Kaur, Sajitha Smiley

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引用次数: 0

摘要

Web服务器维护重要的用户日志文件，记录对它的每个请求。Web日志是事件的记录，其中包括从Web访问者启动会话到会话结束的所有用户详细信息。web使用模式发现，识别用户在web上访问行为的不同状态。提出了一种基于上下文感知的内聚马尔可夫模型和Apriori聚类的web推荐系统设计方法。该算法的预测率高于传统的马尔可夫模型。

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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Web Usage Pattern Detection Using Cohesive Markov Model With Apriori Algorithm

Web server maintains the essential user log files, recording every request to it. Web log is a record of events which includes all the user details from the time the web visitor initiated the session to the end of the session. The web usage pattern discovery to identify different states of the user access behavior on web. The design of web recommender system using a context-aware Cohesive Markov Model and Apriori clustering is proposed. The prediction rate of proposed algorithm is higher than conventional Markov model.

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2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

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