2006 | OriginalPaper | Chapter
A Method for Pinpoint Clustering of Web Pages with Pseudo-Clique Search
Authors : Makoto Haraguchi, Yoshiaki Okubo
Published in: Federation over the Web
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
This paper presents a method for
Pinpoint Clustering
of web pages. We try to find useful clusters of web pages which are significant in the sense that their contents are similar to ones of higher-ranked pages. Since we are usually careless of lower-ranked pages, they are unconditionally discarded even if their contents are similar to some pages with high ranks. Such hidden pages together with significant higher-ranked pages are extracted as a cluster. As the result, our clusters can provide new valuable information for users.
In order to obtain such clusters, we first extract semantic correlations among terms by applying
Singular Value Decomposition
(SVD) to the term-document matrix generated from a corpus. Based on the correlations, we can evaluate potential similarities among web pages to be clustered. The set of web pages is represented as a weighted graph
G
based on the similarities and their ranks. Our clusters can be found as
pseudo-cliques
in
G
. An algorithm for finding Top-
N
weighted pseudo-cliques is presented. Our experimental result shows that a quite valuable cluster can be actually extracted according to our method.
We also discuss an idea for improvement on meanings of clusters. With the help of
Formal Concept Analysis
, our clusters, called FC-based clusters, can be provided with clear meanings. Our preliminary experimentation shows that the extended method would be a promising approach to finding meaningful clusters.