NDLI: PageCluster: Mining conceptual link hierarchies from Web log files for adaptive Web site navigation

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ACM Transactions on Internet Technology (TOIT) : Volume 16

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Issue 4, November 2004

Issue 3, August 2004

Issue 2, May 2004

Guest editorial: Machine learning for the Internet

Learning to find answers to questions on the Web

Selective Markov models for predicting Web page accesses

PageCluster: Mining conceptual link hierarchies from Web log files for adaptive Web site navigation

LinkSelector: A Web mining approach to hyperlink selection for Web portals

Issue 1, February 2004

ACM Transactions on Internet Technology (TOIT) : Volume 3

ACM Transactions on Internet Technology (TOIT) : Volume 2

ACM Transactions on Internet Technology (TOIT) : Volume 1

PageCluster: Mining conceptual link hierarchies from Web log files for adaptive Web site navigation

Content Provider	ACM Digital Library
Author	Hong, Jun Zhu, Jianhan Hughes, John G.
Copyright Year	2004
Abstract	User traversals on hyperlinks between Web pages can reveal semantic relationships between these pages. We use user traversals on hyperlinks as weights to measure semantic relationships between Web pages. On the basis of these weights, we propose a novel method to put Web pages on a Web site onto different conceptual levels in a link hierarchy. We develop a clustering algorithm called PageCluster, which clusters conceptually-related pages on each conceptual level of the link hierarchy based on their in-link and out-link similarities. Clusters are then used to construct a conceptual link hierarchy, which is visualized in a prototype called Online Navigation Explorer (ONE) for adaptive Web site navigation. Our experiments show that our method can put Web pages onto conceptual levels of a link hierarchy more accurately than both the breadth-first search method and the shortest-weighted-path method, and PageCluster can cluster conceptually-related pages more accurately than the bibliographic analysis method. Our user study also shows that the conceptual link hierarchy visualized in ONE can help users find information more effectively and efficiently as the task of finding information becomes less specific and involves more Web pages on multiple conceptual levels.
Starting Page	185
Ending Page	208
Page Count	24
File Format	PDF
ISSN	15335399
e-ISSN	15576051
DOI	10.1145/990301.990305
Volume Number	4
Issue Number	2
Journal	ACM Transactions on Internet Technology (TOIT)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-05-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Link hierarchies Web site navigation Bibliographic analysis Clustering Conceptual link hierarchies Link similarity
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

PageCluster: Mining Conceptual Link (2004)

Using markov models for web site link prediction (2002).

Abstract Mining Web Site Link Structures for Adaptive Web Site Navigation and Search

WEB STRUCTURE REORGANIZATION FOR ADAPTIVE NAVIGATION THROUGH CONCEPTUAL CLUSTERING SANG C. SUH

Clustering and searching WWW images using link and page layout analysis

Towards Adaptive Web Sites: Conceptual Framework and Case Study (2001)

Identifying interesting visitors through Web log classification

A study of web navigation pattern using clustering algorithm in web log files.

Mining Web Site Link Structures for Adaptive Web Site Navigation and Search

PageCluster: Mining conceptual link hierarchies from Web log files for adaptive Web site navigation

Similar Documents

PageCluster: Mining Conceptual Link (2004)

Using markov models for web site link prediction (2002).

Abstract Mining Web Site Link Structures for Adaptive Web Site Navigation and Search

WEB STRUCTURE REORGANIZATION FOR ADAPTIVE NAVIGATION THROUGH CONCEPTUAL CLUSTERING SANG C. SUH

Clustering and searching WWW images using link and page layout analysis

Towards Adaptive Web Sites: Conceptual Framework and Case Study (2001)

Identifying interesting visitors through Web log classification

A study of web navigation pattern using clustering algorithm in web log files.

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PageCluster: Mining conceptual link hierarchies from Web log files for adaptive Web site navigation