Luis A. Leiva
Enrique Vidal
ABSTRACT
In this paper we are interested in describing Web pages by how users interact within their contents. Thus, an alternate but complementary way of labelling and classifying Web documents is introduced. The proposed methodology is founded on unsupervised learning algorithms, aiming to automatically find natural clusters by means of users' implicit interaction data. Furthermore, it also copes with the dynamic nature and heterogeneity of both users' behaviour and the Web, updating the clustering model over time. We want to show that our framework can be easily integrated in any Website, just employing already-known methods and current technologies.
- Q. Guoand E. Agichtein. Exploring mouse movements for inferring query intent. In Proc. SIGIR, pages 707--708, 2008. Google Scholar
Digital Library
- L. A. Leivaand R. Vivó. A gesture inference methodology or user evaluation based on mouse activity tracking. In Proc. IHCI, pages 58--67, 2008.Google Scholar
- Y. Liu, X. Huang, A. An, and G. Promhouse. Clustering web surfers with probabilistic models in a real application. In Proc. WI, pages 761--765, 2004. Google ScholarDigital Library
- M. Spiliopoulou and C. Pohle. Datamining form easuring and improving the success of websites. Data Mining and Knowledge Discovery, 5 (1): 85--114, 2003. Google ScholarDigital Library
Index Terms
- Assessing users' interactions for clustering web documents: a pragmatic approach
Recommendations
Interval Set Clustering of Web Users with Rough K-Means
Data collection and analysis in web mining faces certain unique challenges. Due to a variety of reasons inherent in web browsing and web logging, the likelihood of bad or incomplete data is higher than conventional applications. The analytical ...
Time-Aware Web Users' Clustering
Web users clustering is a crucial task for mining information related to users needs and preferences. Up to now, popular clustering approaches build clusters based on usage patterns derived from users' page preferences. This paper emphasizes the need to ...
Fuzzy C-means clustering of web users for educational sites
AI'03: Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligenceCharacterization of users is an important issue in the design and maintenance of websites. Analysis of the data from the World Wide Web faces certain challenges that are not commonly observed in conventional data analysis. The likelihood of bad or ...
Comments