PNP: mining of profile navigational patterns

Hua-Fu Li; Man-Kwan Shan

doi:10.1117/12.460235

12 March 2002 PNP: mining of profile navigational patterns

Hua-Fu Li, Man-Kwan Shan

Proceedings Volume 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV; (2002) https://doi.org/10.1117/12.460235
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

Web usage mining is a key knowledge discovery research and as such has been well researched. So far, this research has focused mainly on databases containing access log data only. However, many real-world databases contain users profile data and current solutions for this situation are still insufficient. In this paper we have a large database containing of user profile information together with user web-pages navigation patterns. The user profile data includes quantitative attributes, such as salary or age, and categorical attributes, such as sex or marital status. We introduce the concept of profile navigation patterns, which discusses the problem of relating user profile information to navigational behavior. An example of such profile navigation pattern might be 20% of married people between age 25 and 30 have the similar navigational behavior <(a,c)(c,h)(h,i)(i,h)(h,l)>, where a, c, h, i, l are web pages in a web site. The navigation patterns may contain the generic traversal behavior, e.g. trend to backward moves, cycles etc. The objective of mining profile navigation patterns is to identify browser profile for web personalization. We give an algorithm for mining such profile navigation patterns. Our method (algorithm PNP) can discover profile navigation patterns efficiently. We also present new inclination measurements to identify the interesting profile navigational patterns. Experimental results show the efficiency and scalability of PNP.

Citation Download Citation

Hua-Fu Li and Man-Kwan Shan "PNP: mining of profile navigational patterns", Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); https://doi.org/10.1117/12.460235

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Mining

Databases

Data mining

Knowledge discovery

Computer science

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