Self-Organizing Maps
Very Large Two-Level SOM for the Browsing of Newsgroups
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Creating Adaptive Web Sites Through Usage-Based Clustering of URLs
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for architecture, design, and implementation
Cluster Analysis for Data Mining and System Identification
Cluster Analysis for Data Mining and System Identification
Building an Intelligent Web: Thoery and Practice
Building an Intelligent Web: Thoery and Practice
| Hi-index | 0.00 |
In order to understand the behavior of website users, a deep analysis of content and usage data can reveal valuable knowledge about the main subjects these visitors are truly interested in. Preprocessing and clustering the highly unstructured content of web pages should be addressed very carefully in order to provide effective results. In this paper, a novel proposed two-phase self organizing feature map clustering framework to segment web users based on their subject interests in the diverse content of a University website is described. Also, the overall noise and dimensionality reduction of the sample web site content is properly addressed through the formulation of a comprehensive ten-step preprocessing procedure, which provided very promising experimental results when applied to the input web pages in the first phase of the proposed framework.