Abstract
This chapter provides a survey of some clustering methods relevant to clustering Web elements for better information access. We start with classical methods of cluster analysis that seems to be relevant in approaching the clustering of Web data.
Graph clustering is also described since its methods contribute significantly to clustering Web data. The use of artificial neural networks for clustering has the same motivation.
Based on previously presented material, the core of the chapter provides an overview of approaches to clustering in the Web environment. Particularly, we focus on clustering Web search results, in which clustering search engines arrange the search results into groups around a common theme.
We conclude with some general considerations concerning the justification of so many clustering algorithms and their application in the Web environment.