2021•Unpublished venueRequires access

Web User’s Access Pattern Identification Using Clustering Algorithms: A Survey

Prerna Kumari, Aneesh Kumar Mishra, Rahul Kumar

Open publisher page 1 citations

Abstract

Users of web site usually perform their interest oriented actions by clicking or visiting web pages, which are traced in access log files. The clustering web client's access pattern may catch common client interest in a website. The conventional web usage mining techniques for clustering web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users' navigational behaviour. One of the best tasks of internet usage mining, web client grouping, which sets up clusters of clients displaying comparative browsing designs gives beneficial information to customized web benefits. This paper presents a survey covering the strategies and techniques of clustering algorithms and demanding situations appear within the area.

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What this paper is about

Users of web site usually perform their interest oriented actions by clicking or visiting web pages, which are traced in access log files. The clustering web client's access pattern may catch common client interest in a website. The conventional web usage mining techniques for clustering web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users' navigational behaviour. One of the best tasks of internet usage mining, web client grouping, which sets up clusters of clients displaying comparative browsing designs gives beneficial information to customized web benefits. This paper presents a survey covering the strategies and techniques of clustering algorithms and demanding situations appear within the area.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Users of web site usually perform their interest oriented actions by clicking or visiting web pages, which are traced in access log files. The clustering web client's access pattern may catch common client interest in a website. The conventional web usage mining techniques for clustering web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users' navigational behaviour. One of the best tasks of internet usage mining, web client grouping, which sets up clusters of clients displaying comparative browsing designs gives beneficial information to customized web benefits. This paper presents a survey covering the strategies and techniques of clustering algorithms and demanding situations appear within the area.

Key concepts: Computer science, Web mining, Cluster analysis, World Wide Web, Web navigation, Web page, Web modeling, Data Web

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