A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites

Nasraoui, Olfa; Soliman, Maha; Saka, Esin; Badia, Antonio; Germain, Richard

doi:10.1109/tkde.2007.190667

Cited by 166 publications

(93 citation statements)

References 23 publications

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“…Nasraoui, et al, [19] presented a framework for discovering and tracking evolving user profiles in real-time environment using Web usage mining and Web ontology. Preprocessing is first performed on the Web log data to identify user sessions.…”

Section: Related Workmentioning

confidence: 99%

Bidirectional Growth Based Mining and Cyclic Behaviour Analysis of Web Sequential Patterns

Srikantaiah¹,

Kumar²,

Venugopal³

et al. 2013

IJDKP

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Section: Related Workmentioning

confidence: 99%

Bidirectional Growth Based Mining and Cyclic Behaviour Analysis of Web Sequential Patterns

Srikantaiah¹,

Kumar²,

Venugopal³

et al. 2013

IJDKP

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“…Different modes of usage or mass user profiles can be discovered using Web usage mining techniques that can automatically extract frequent access patterns from the history of previous user clickstreams stored in Web log files [24]. These profiles can later be harnessed towards personalizing the Web site to the user.…”

Section: Related Workmentioning

confidence: 99%

Applying Web Usage Mining for Adaptive Intranet Navigation

Saad

Kruschwitz

2011

Lecture Notes in Computer Science

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Abstract. Much progress has recently been made in assisting a user in the search process, be it Web search where the big search engines have now all incorporated more interactive features or be it online shopping where customers are commonly recommended items that appear to match the customer's interest. While assisted Web search relies very much on implicit information such as the users' search behaviour, recommender systems typically rely on explicit information, expressed for example by a customer purchasing an item. Surprisingly little progress has however been made in making navigation of a Web site more adaptive. Web sites can be difficult to navigate as they tend to be rather static and a new user has no idea what documents are most relevant to his or her need. We try to assist a new user by exploiting the navigation behaviour of previous users. On a university Web site for example, the target users change constantly. In a company the change might not be that dramatic, nevertheless new employees join the company and others retire. What we propose is to make the Web site more adaptive by introducing links and suggestions to commonly visited pages without changing the actual Web site. We simply add a layer on top of the existing site that makes recommendations regarding links found on the page or pages that are further away but have been typical landing pages whenever a user visited the current Web page. This paper reports on a task-based evaluation that demonstrates that the idea is very effective. Introducing suggestions as outlined above was found to be not just preferred by the users of our study but allowed them also to get to the results more quickly.

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“…In the pattern discovery phase, Special pattern discovery algorithms applied on raw data which is output of the data processing phase [3,7]. In the pattern analysis phase interesting knowledge is extracted from frequent patterns and these results are used in various applications such as personalization, system improvement, site modification.…”

Section: Web Usage Miningmentioning

confidence: 99%

“…Web usage mining has various application areas such as web pre-fetching, link prediction, site reorganization and web personalization [1, 2, and 14]. Most important phases of web usage mining are the [2,3] reconstruction of user sessions by using heuristics techniques and discovering useful patterns from these sessions by using pattern discovery techniques like association rule mining, Apriori etc [4,3]. We propose an integrated system (Web Tool) for applying data mining [16] techniques such as association rules or sequential patterns on access log files.…”

Section: Introductionmentioning

confidence: 99%

Efficient Web Log Mining Using Enhanced Apriori Algorithm with Hash Tree and Fuzzy

Veeramalai¹,

Jaisankar²,

Kannan³

et al. 2010

IJCSIT

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Web usage mining is the type of Web mining activity that involves the automatic discovery of user access patterns from one or more Web servers. In this paper we analyze the pattern using different algorithms like Apriori, Hash tree and Fuzzy and then we used enhanced Apriori algorithm to give the solution for CrispBoundry problem with higher optimized efficiency while comparing to other algorithms.

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A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites

Cited by 166 publications

References 23 publications

Bidirectional Growth Based Mining and Cyclic Behaviour Analysis of Web Sequential Patterns

Bidirectional Growth Based Mining and Cyclic Behaviour Analysis of Web Sequential Patterns

Applying Web Usage Mining for Adaptive Intranet Navigation

Efficient Web Log Mining Using Enhanced Apriori Algorithm with Hash Tree and Fuzzy

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