Association Rules in Web Usage Logfile Data – Empirical Insights into the Use of User-Generated Web Site Features

2013 ◽  
pp. 50-62 ◽  
Author(s):  
Christian Holsing ◽  
Carsten D. Schultz
Keyword(s):  
Association Rules ◽  
Web Site ◽  
Web Usage

Web Usage Mining through Associative Models

2011 ◽  
pp. 1231-1234 ◽  
Author(s):  
Paolo Giudici ◽  
Paola Cerchiello
Keyword(s):  
Web Site ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Page ◽  
User Session ◽  
Web Usage ◽  
Associative Models ◽  
The Web

The aim of this contribution is to show how the information, concerning the order in which the pages of a Web site are visited, can be profitably used to predict the visit behaviour at the site. Usually every click corresponds to the visualization of a Web page. Thus, a Web clickstream defines the sequence of the Web pages requested by a user. Such a sequence identifies a user session.

Web usage mining for Web site evaluation

2000 ◽  
Vol 43 (8) ◽  
pp. 127-134 ◽  
Author(s):  
Myra Spiliopoulou
Keyword(s):  
Web Site ◽  
Web Usage Mining ◽  
Web Usage ◽  
Site Evaluation

Using Association Rules and Markov Model for Predit Next Access on Web Usage Mining

2007 ◽  
pp. 371-376 ◽  
Author(s):  
Siriporn Chimphlee ◽  
Naomie Salim ◽  
Mohd Salihin Bin Ngadiman ◽  
Witcha Chimphlee
Keyword(s):  
Markov Model ◽  
Association Rules ◽  
Web Usage Mining ◽  
Web Usage

scholarly journals Web usage mining with evolutionary extraction of temporal fuzzy association rules

2013 ◽  
Vol 54 ◽  
pp. 66-72 ◽  
Author(s):  
Stephen G. Matthews ◽  
Mario A. Gongora ◽  
Adrian A. Hopgood ◽  
Samad Ahmadi
Keyword(s):  
Association Rules ◽  
Web Usage Mining ◽  
Fuzzy Association Rules ◽  
Web Usage

An Efficient Genetic Simulated Annealing Association Rules Method

2010 ◽  
Vol 34-35 ◽  
pp. 927-931
Author(s):  
Jun Jie Cen ◽  
Guo Hong Gao ◽  
Ying Jun Wang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Data Analysis ◽  
Association Rules ◽  
Association Rule ◽  
Web Site ◽  
Web Mining ◽  
Incremental Mining ◽  
Mining Technology

Association rule is one of the important models of Web mining. By analyzing the topology of web site, this paper brings forward an efficient genetic simulated annealing association rules method．It applies genetic algorithm，incremental mining technology to trace users access behavior and optimizes association rules，and forecast capable association rules which improves its precision．Finally, this paper gives out the data analysis of experiment and summarizes the characteristics of genetic mining．

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