He X, Zha HY, Ding CHQ, Simon HD "Web document clustering using hyperlink structures" COMPUTATIONAL STATISTICS & DATA ANALYSIS 41 (1): 19-45 NOV 28 2002

[Eugene Garfield]

Xiaopeng HE : {xhe,zha}@cse.psu.edu


Title     Web document clustering using hyperlink structures
Author    He X, Zha HY, Ding CHQ, Simon HD
Journal   COMPUTATIONAL STATISTICS & DATA ANALYSIS 41 (1): 19-45 NOV 28 2002

 Document type: Article    Language : English
 Cited References : 35     Times Cited: 0


Abstract:
With the exponential growth of information on the World Wide Web, there is
great demand for developing efficient methods for effectively organizing the
large amount of retrieved information. Document clustering plays an
important role in information retrieval and taxonomy management for the Web.
In this paper we examine three clustering methods: K-means, multi-level
METIS, and the recently developed normalized-cut-method using a new approach
of combining textual information, hyperlink structure and co-citation
relations into a single similarity metric. We found the normalized-cut
method with the new similarity metric is particularly effective, as
demonstrated on three datasets of web query results. We also explore some
theoretical connections between the normalized-cut method and the K-means
method. (C) 2002 Elsevier Science B.V. All rights reserved.

Author Keywords:
World Wide Web, graph partitioning, cheeger constant, clustering method,
K-means method, normalized cut method, eigenvalue decomposition, link
structure,
similarity metric

KeyWords Plus:
GRAPHS, EIGENVECTORS, ALGORITHM, MATRICES

Addresses:
Penn State Univ, Dept Comp Sci & Engn, University Pk, PA 16802 USA
Univ Calif Berkeley, Lawrence Berkeley Lab, NERSC Div, Berkeley, CA 94720
USA

Publisher:
ELSEVIER SCIENCE BV, AMSTERDAM

IDS Number:
615NV

ISSN:
0167-9473


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