research-article

A comparative evaluation of different link types on enhancing document clustering

Authors:

Xiaohua ZhouAuthors Info & Claims

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 555 - 562

https://doi.org/10.1145/1390334.1390429

Published: 20 July 2008 Publication History

Abstract

With a growing number of works utilizing link information in enhancing document clustering, it becomes necessary to make a comparative evaluation of the impacts of different link types on document clustering. Various types of links between text documents, including explicit links such as citation links and hyperlinks, implicit links such as co-authorship links, and pseudo links such as content similarity links, convey topic similarity or topic transferring patterns, which is very useful for document clustering. In this study, we adopt a Relaxation Labeling (RL)-based clustering algorithm, which employs both content and linkage information, to evaluate the effectiveness of the aforementioned types of links for document clustering on eight datasets. The experimental results show that linkage is quite effective in improving content-based document clustering. Furthermore, a series of interesting findings regarding the impacts of different link types on document clustering are discovered through our experiments.

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Cited By

Aouadi HKhemakhem MJemaa M(2019)Uncovering Hidden Links Between Images Through Their Textual ContextEnterprise Information Systems10.1007/978-3-030-26169-6_18(370-395)Online publication date: 28-Jul-2019
https://doi.org/10.1007/978-3-030-26169-6_18
Patil HThakur R(2018)Document ClusteringInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch003(47-64)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch003
You ZQian T(2018)Learning a Joint Representation for Classification of Networked DocumentsNeural Information Processing10.1007/978-3-030-04221-9_18(199-209)Online publication date: 17-Nov-2018
https://doi.org/10.1007/978-3-030-04221-9_18
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Index Terms

A comparative evaluation of different link types on enhancing document clustering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

July 2008

934 pages

ISBN:9781605581644

DOI:10.1145/1390334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Mun-Kew Leong
National Library Board, Singapore
,
Program Chairs:
Syung Hyon Myaeng
Information and Communications University, Korea
,
Douglas W. Oard
University of Maryland, College Park, USA
,
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy

Copyright © 2008 ACM.

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Published: 20 July 2008

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SIGIR '08: The 31st Annual International ACM SIGIR Conference

July 20 - 24, 2008

Singapore, Singapore

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Aouadi HKhemakhem MJemaa M(2019)Uncovering Hidden Links Between Images Through Their Textual ContextEnterprise Information Systems10.1007/978-3-030-26169-6_18(370-395)Online publication date: 28-Jul-2019
https://doi.org/10.1007/978-3-030-26169-6_18
Patil HThakur R(2018)Document ClusteringInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch003(47-64)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch003
You ZQian T(2018)Learning a Joint Representation for Classification of Networked DocumentsNeural Information Processing10.1007/978-3-030-04221-9_18(199-209)Online publication date: 17-Nov-2018
https://doi.org/10.1007/978-3-030-04221-9_18
Patil HThakur R(2017)Document ClusteringPattern and Data Analysis in Healthcare Settings10.4018/978-1-5225-0536-5.ch013(264-281)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0536-5.ch013
Kozlowski M(2016)Study Fields Clustering Using KRK CompetencesMachine Intelligence and Big Data in Industry10.1007/978-3-319-30315-4_4(35-47)Online publication date: 25-Mar-2016
https://doi.org/10.1007/978-3-319-30315-4_4
Kozlowski M(2015)Web Search Results Clustering Using Frequent Termset MiningPattern Recognition and Machine Intelligence10.1007/978-3-319-19941-2_50(525-534)Online publication date: 23-Jun-2015
https://doi.org/10.1007/978-3-319-19941-2_50
Liu MLiu YLiu BLin L(2013)Probability-based text clustering algorithm by alternately repeating two operationsJournal of Information Science10.1177/016555151247005439:3(372-383)Online publication date: 29-Jan-2013
https://doi.org/10.1177/0165551512470054
Di Marco ANavigli R(2013)Clustering and Diversifying Web Search Results with Graph-Based Word Sense InductionComputational Linguistics10.1162/COLI_a_0014839:3(709-754)Online publication date: Sep-2013
https://doi.org/10.1162/COLI_a_00148
He ZZhang ZYu P(2012)Overlapping community detection combining content and linkJournal of Zhejiang University SCIENCE C10.1631/jzus.C120004913:11(828-839)Online publication date: 8-Nov-2012
https://doi.org/10.1631/jzus.C1200049
Zhang XHu XHu TPark EZhou X(2012)Utilizing Different Link Types to Enhance Document Clustering Based on Markov Random Field Model With Relaxation LabelingIEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans10.1109/TSMCA.2012.218718342:5(1167-1182)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1109/TSMCA.2012.2187183
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