Adaptive clustering of hypermedia documents

摘要

A hypermedia system connects various types of information into a network where related nodes of information (text, audio, video) are connected by links. Clustering these nodes is an effective way to reduce information-overhead, allowing the user to browse through the clusters as well as the individual nodes. In this paper, we compare the use of two adaptive algorithms (genetic algorithms, and neural networks) in clustering hypermedia documents. These clusters allow the user to index into this overwhelming number of nodes and find needed information quickly. We base the clustering on the user's paths through the hypermedia document and not on the content of the nodes or the structure of the links in the document, thus the clustering reflects the unique relationships each user sees among the nodes. The original hypermedia document remains untouched, however each user will now have a personalized index into this document.