Details
| Original language | English |
|---|---|
| Pages (from-to) | 867-885 |
| Number of pages | 19 |
| Journal | Data and Knowledge Engineering |
| Volume | 68 |
| Issue number | 10 |
| Early online date | 19 May 2009 |
| Publication status | Published - Oct 2009 |
Abstract
As one of the most important tasks of Web Usage Mining (WUM), web user clustering, which establishes groups of users exhibiting similar browsing patterns, provides useful knowledge to personalized web services and motivates long term research interests in the web community. Most of the existing approaches cluster web users based on the snapshots of web usage data, although web usage data are evolutionary in the nature. Consequently, the usefulness of the knowledge discovered by existing web user clustering approaches might be limited. In this paper, we address this problem by clustering web users based on the evolution of web usage data. Given a set of web users and their associated historical web usage data, we study how their usage data change over time and mine evolutionary patterns from each user's usage history. The discovered patterns capture the characteristics of changes to a web user's information needs. We can then cluster web users by analyzing common and similar evolutionary patterns shared by users. Web user clusters generated in this way provide novel and useful knowledge for various personalized web applications, including web advertisement and web caching.
Keywords
- Evolutionary pattern mining, Historical web session, Web Usage Mining, Web user clustering
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
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In: Data and Knowledge Engineering, Vol. 68, No. 10, 10.2009, p. 867-885.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - COWES
T2 - Web user clustering based on evolutionary web sessions
AU - Chen, Ling
AU - Bhowmick, Sourav S.
AU - Nejdl, Wolfgang
PY - 2009/10
Y1 - 2009/10
N2 - As one of the most important tasks of Web Usage Mining (WUM), web user clustering, which establishes groups of users exhibiting similar browsing patterns, provides useful knowledge to personalized web services and motivates long term research interests in the web community. Most of the existing approaches cluster web users based on the snapshots of web usage data, although web usage data are evolutionary in the nature. Consequently, the usefulness of the knowledge discovered by existing web user clustering approaches might be limited. In this paper, we address this problem by clustering web users based on the evolution of web usage data. Given a set of web users and their associated historical web usage data, we study how their usage data change over time and mine evolutionary patterns from each user's usage history. The discovered patterns capture the characteristics of changes to a web user's information needs. We can then cluster web users by analyzing common and similar evolutionary patterns shared by users. Web user clusters generated in this way provide novel and useful knowledge for various personalized web applications, including web advertisement and web caching.
AB - As one of the most important tasks of Web Usage Mining (WUM), web user clustering, which establishes groups of users exhibiting similar browsing patterns, provides useful knowledge to personalized web services and motivates long term research interests in the web community. Most of the existing approaches cluster web users based on the snapshots of web usage data, although web usage data are evolutionary in the nature. Consequently, the usefulness of the knowledge discovered by existing web user clustering approaches might be limited. In this paper, we address this problem by clustering web users based on the evolution of web usage data. Given a set of web users and their associated historical web usage data, we study how their usage data change over time and mine evolutionary patterns from each user's usage history. The discovered patterns capture the characteristics of changes to a web user's information needs. We can then cluster web users by analyzing common and similar evolutionary patterns shared by users. Web user clusters generated in this way provide novel and useful knowledge for various personalized web applications, including web advertisement and web caching.
KW - Evolutionary pattern mining
KW - Historical web session
KW - Web Usage Mining
KW - Web user clustering
UR - http://www.scopus.com/inward/record.url?scp=69249222586&partnerID=8YFLogxK
U2 - 10.1016/j.datak.2009.05.002
DO - 10.1016/j.datak.2009.05.002
M3 - Article
AN - SCOPUS:69249222586
VL - 68
SP - 867
EP - 885
JO - Data and Knowledge Engineering
JF - Data and Knowledge Engineering
SN - 0169-023X
IS - 10
ER -