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http://dx.doi.org/10.9723/jksiis.2013.18.2.047

Mining Association Rules from the Web Access Log of an Online News website  

Hwang, Hyunseok (한림대학교 경영학부, 한림경영연구소)
Yoo, Keedong (단국대학교 경영학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.18, no.2, 2013 , pp. 47-57 More about this Journal
Abstract
Today a lot of functional areas of a firm are operated on the Web. Online shopping malls analyze web log recording customers' activities on the web to connect them to business outcomes. Not only commercial websites, but online news sites also need to collect and analyze web logs to understand their news readers' interest. However, little research has been performed yet. In this research we mined the web access log of an online news website and conduct Market Basket Analysis to uncover the association rules among the categories of news articles. The research is composed of two stages: 1) Identifying the individual session of a visitor; 2) Mining association rule from news articles read by each session. We gather 7-day access logs two times. The results of log mining and meanings of association rules are suggested with managerial implications in conclusion section.
Keywords
Web log; Association rule; Data mining; News website; Industrial Information;
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