A Generalization-Based Approach to Clustering of Web Usage Sessions

Fu, Yongjian; Sandhu, Kanwalpreet; Shih, Ming-Yi

doi:10.1007/3-540-44934-5_2

Cited by 78 publications

(31 citation statements)

References 9 publications

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“…Third, there exists a large number of tools to analyze clickstream data, and those tools could be classified based on the metrics they use to interpret the collected data [5]. The tools may use Web metric-based methodologies [13], basic marketing metricbased methodologies, navigation-based methodologies [14], or traffic-based methodologies [15]. These metrics differ depending on the perspective each tool looks at a webpage, and the targeted types of web services and applications.…”

Section: Related Workmentioning

confidence: 99%

A framework for identifying the linkability between Web servers for enhanced internet computing and E-commerce

Artail

Halabi

Hachem

et al. 2017

J Internet Serv Appl

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Existing Web session tracking techniques mainly work on huge web server log files, which limit their dynamicity and capability. We present a collaborative method for session tracking to identify direct and indirect web server interactions. The outcome could lead to building partnerships between organizations that do e-business, adapting links on websites to user click behavior, and deciding on the particular services offered by websites. We implemented a prototype that showed the usefulness and effectiveness of the system. We describe the significance and applications of our approach in e-commerce through information that describes traffic flows between websites.

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Section: Related Workmentioning

confidence: 99%

A framework for identifying the linkability between Web servers for enhanced internet computing and E-commerce

Artail

Halabi

Hachem

et al. 2017

J Internet Serv Appl

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“…As this method models sessions in a finer degree of granularity, there is a potential scalability problem. Fu et al [5,6,7] grouped pages with the same URL prefix to reduce the number of different pages in a session before applying the clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

Characterizing customer groups for an e-commerce website

Wang

Makaroff

Edwards

2004

Proceedings of the 5th ACM Conference on Electronic Commerce

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In conventional commerce, customer groups with similar interests or behaviours can be observed. Similarly, customers in E-commerce naturally form groups. These groups allow the organization to provide quality of service (QoS) and perform capacity planning. From a system point of view, overall server performance can be improved and resources managed considering customer session behaviour.Previous studies have grouped customers using clustering techniques. Different data metrics have been selected as criteria for grouping, in order to analyze different problems. The limitation for these approaches is that problems are analyzed separately. In order to manage an E-commerce server well, we must analyze many related problems comprehensively rather than separately. For example, we would like to know the impact on resource usage when optimizing revenue. Thus, we must understand the differences and similarities between session groups chosen by different metrics. This paper characterizes customer groups for an E-rental business and compares customer groups created according to different criteria including services requested, navigation pattern and resource usage. A significant finding of this study shows that using each of the three criteria independently yields roughly similar results, since customers looking for similar services tend to have similar navigation pattern as well as similar server resource usage. Thus, it is sufficient to group customers in only one of these ways. Grouping customers by services requested is suggested since this method yields relatively better results and is simple to implement.

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“…The problem of clustering Web users according to their common browsing activities is investigated in [8]. The approach combines attribute-oriented induction and clustering to find groups of users who share common trends on some themes of pages.…”

Section: Introductionmentioning

confidence: 99%

Mining User Preferences, Page Content and Usage to Personalize Website Navigation

et al. 2005

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The growing availability of information on the Web has raised a challenging problem: can a Web-based information system tailor itself to different user requirements with the ultimate goal of personalizing and improving the users' experience in accessing the contents of a website? This paper proposes a new approach to website personalization based on the exploitation of user browsing interests together with content and usage similarities among Web pages. The outcome is the delivery of page recommendations which are strictly related to the navigational purposes of visitors and their actual location within the cyberspace of the website. Our approach has been used effectively for developing a non-invasive system which allows Web users to navigate through potentially interesting pages without having a basic knowledge of the website structure.

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A Generalization-Based Approach to Clustering of Web Usage Sessions

Cited by 78 publications

References 9 publications

A framework for identifying the linkability between Web servers for enhanced internet computing and E-commerce

A framework for identifying the linkability between Web servers for enhanced internet computing and E-commerce

Characterizing customer groups for an e-commerce website

Mining User Preferences, Page Content and Usage to Personalize Website Navigation

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