Article

A comprehensive comparison study of document clustering for a biomedical digital library MEDLINE

Authors:

Xiaohua HuAuthors Info & Claims

JCDL '06: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries

Pages 220 - 229

https://doi.org/10.1145/1141753.1141802

Published: 11 June 2006 Publication History

Abstract

Document clustering has been used for better document retrieval, document browsing, and text mining in digital library. In this paper, we perform a comprehensive comparison study of various document clustering approaches such as three hierarchical methods (single-link, complete-link, and complete link), Bisecting K-means, K-means, and Suffix Tree Clustering in terms of the efficiency, the effectiveness, and the scalability. In addition, we apply a domain ontology to document clustering to investigate if the ontology such as MeSH improves clustering qualify for MEDLINE articles. Because an ontology is a formal, explicit specification of a shared conceptualization for a domain of interest, the use of ontologies is a natural way to solve traditional information retrieval problems such as synonym/hypernym/ hyponym problems. We conducted fairly extensive experiments based on different evaluation metrics such as misclassification index, F-measure, cluster purity, and Entropy on very large article sets from MEDLINE, the largest biomedical digital library in biomedicine.

References

[1]

Aggarwal, C. C., Wolf, J. L., Yu, P. S., Procopiuc, C., and Park, J. S. Fast algorithms for projected clustering. In Proceedings of the 1999 ACM SIGMOD International Conference on Management of data, 1999, 61--72.]]

Digital Library

[2]

Beil, F., Ester, M. and Xu, X. Frequent Term-Based Text Clustering, In Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23--26, 2002, Edmonton, Alberta, Canada, 436--442.]]

Digital Library

[3]

Beyer, K., Goldstein, J., Ramakrishnan, R., and Shaft, U. When is nearest neighbor meaningful?. Proceedings of 7th International Conference on Database Theory, 1999, 217--235.]]

Digital Library

[4]

Buckley, C., Salton, G., Allen, J. and Singhal, A. Automatic query expansion using SMART: TREC-3. In: D. K. Harman (ed.), The Third Text Retrieval Conference (TREC-3). U.S. Department of Commerce, 1995, 69--80.]]

[5]

Buckley, C. and Lewit, A. F. Optimization of inverted vector searches. In Proceedings of SIGIR-85, 1985, 97--110.]]

Digital Library

[6]

Cutting, D., Karger, D., Pedersen, J. and Tukey, J. Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections, In Proceedings of SIGIR '92, 1992, 318--329.]]

Digital Library

[7]

Ghosh, J. Scalable clustering methods for data mining. In N. Ye (Ed.), Handbook of data mining. Lawrence Erlbaum, 2003.]]

[8]

Gruber, T.R. Towards Principles for the Design of Ontologies used for Knowledge Sharing. International Journal of Human-Computer Studies, 43, 1995, 907--928.]]

Digital Library

[9]

Hearst, M. A. and Pedersen, J. O. Reexamining the cluster hypothesis: Scatter/Gather on retrieval results. In Proceedings of SIGIR-96, 1996, 76--84.]]

Digital Library

[10]

Hotho, A., Maedche A., and Staab S. Text Clustering Based on Good Aggregations. Künstliche Intelligenz (KI), 16, 4, 2002, 48--54.]]

[11]

Hu, X. Mining Novel Connections from Large Online Digital Library Using Biomedical Ontologies, Library Management Journal, 26, 4/5, 2005, 261--270.]]

[12]

Kaufman, L., and Rousseeuw, P.J. Finding Groups in Data: an Introduction to Cluster Analysis, 1999, John Wiley & Sons.]]

[13]

Koller, D. and Sahami, M. Hierarchically classifying documents using very few words. In Proceedings of ICML-97, 1997, 170--176.]]

Digital Library

[14]

Larsen, B. and Aone, C. Fast and Effective Text Mining Using Linear-time Document Clustering, KDD-99, San Diego, California, 1999, 16--22.]]

Digital Library

[15]

Li, T., Ma, S., and Ogihara, M. Document clustering via adaptive subspace iteration. In Proceedings of the 2004 ACM SIGMOD International Conference on Management of data, 2004, 218--225.]]

Digital Library

[16]

Pantel, P. and Lin, D. Document clustering with committees. In Proceedings of the 2002 ACM SIGMOD International Conference on Management of data, 2002, 199--206.]]

Digital Library

[17]

Steinbach, M., Karypis, G., and Kumar, V. A Comparison of Document Clustering Techniques. Technical Report #00-034. Department of Computer Science and Engineering, University of Minnesota, 2000.]]

[18]

van Rijsbergen, C. J. Information Retrieval, 2nd edition, London: Buttersworth, 1979. (http://www.dcs.gla.ac.uk/Keith/Preface.html)]]

Digital Library

[19]

Wang, B.B., McKay, R I., Abbass, H.A., Barlow M. Learning Text Classifier using the Domain Concept Hierarchy. In Proceedings of International Conference on Communications, Circuits and Systems 2002, China.]]

[20]

Willett, P. Recent trends in hierarchical document clustering: A critical review. Information Processing & Management, 24, 5, 1988, 577--597.]]

Digital Library

[21]

Xu, W. and Gong, Y. Document clustering by concept factorization. Proceedings of SIGIR-04, 2004, 202--209.]]

Digital Library

[22]

Zamir O., and Etzioni O. Web Document Clustering: A Feasibility Demonstration, In Proceedings of SIGIR 98, 1998, 46--54.]]

Digital Library

[23]

Zeng, Y., Tang, J., Garcia-Frias, J. and Gao, G.R. An Adaptive Meta-Clustering Approach: Combining The Information From Different Clustering Results, IEEE Computer Society Bioinformatics Conference (CSB2002), 2002, 276--287.]]

Digital Library

[24]

Zhao, Y., and Karypis, G. Criterion functions for document clustering: Experiments and analysis, Technical Report, Department of Computer Science, University of Minnesota, 2002.]]

[25]

Zhao, Y., and Karypis, G. Evaluation of Hierarchical Clustering Algorithms for Document Datasets, Technical Report, Department of Computer Science, University of Minnesota, 2002.]]

[26]

Zhong, S., and Ghosh, J. A comparative study of generative models for document clustering. Proceedings of the workshop on Clustering High Dimensional Data and Its Applications in SIAM Data Mining Conference, 2003.]]

[27]

zu Eissen, S.M., Stein, B, Potthast, M. The Suffix Tree Document Model Revisited, In Proceedings of the 5th International Conference on Knowledge Management, 2005, 596--603.]]

Cited By

Watanabe SIto HMatsubara MMorishima A(2024)Human-in-the-loop latent space learning for biblio-record-based literature managementInternational Journal on Digital Libraries10.1007/s00799-023-00389-825:1(123-136)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00799-023-00389-8
Watanabe SIto HMatsubara MMorishima A(2022)Bibrecord-Based Literature Management with Interactive Latent Space LearningFrom Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries10.1007/978-3-031-21756-2_13(155-171)Online publication date: 7-Dec-2022
https://doi.org/10.1007/978-3-031-21756-2_13
Kaya MSchoop M(2021)Analytical Comparison of Clustering Techniques for the Recognition of Communication PatternsGroup Decision and Negotiation10.1007/s10726-021-09758-731:3(555-589)Online publication date: 13-Oct-2021
https://doi.org/10.1007/s10726-021-09758-7
Show More Cited By

Index Terms

A comprehensive comparison study of document clustering for a biomedical digital library MEDLINE
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Recommendations

Biomedical ontology improves biomedical literature clustering performance: a comparison study

Document clustering has been used for better document retrieval and text mining. In this paper, we investigate if a biomedical ontology improves biomedical literature clustering performance in terms of the effectiveness and the scalability. For this ...
Hybrid Bisect K-Means Clustering Algorithm
BCGIN '11: Proceedings of the 2011 International Conference on Business Computing and Global Informatization

In this paper, we present a hybrid clustering algorithm that combines divisive and agglomerative hierarchical clustering algorithm. Our method uses bisect K-means for divisive clustering algorithm and Unweighted Pair Group Method with Arithmetic Mean (...
Integrating biomedical literature clustering and summarization approaches using biomedical ontology
TMBIO '06: Proceedings of the 1st international workshop on Text mining in bioinformatics

We introduce a method that integrates biomedical literature clustering and summarization using biomedical ontology. The core of the approach is to identify document cluster models as semantic chunks capturing the core semantic relationships in the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

JCDL '06: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries

June 2006

402 pages

ISBN:1595933549

DOI:10.1145/1141753

General Chair:
Gary Marchionini
University of North Carolina, Chapel Hill
,
Program Chairs:
Michael L. Nelson
Old Dominion University
,
Catherine C. Marshall
Microsoft

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

JCDL06

Sponsor:

JCDL06: Joint Conference on Digital Libraries 2006

June 11 - 15, 2006

NC, Chapel Hill, USA

Acceptance Rates

Overall Acceptance Rate 415 of 1,482 submissions, 28%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

32
Total Citations
View Citations
1,003
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Watanabe SIto HMatsubara MMorishima A(2024)Human-in-the-loop latent space learning for biblio-record-based literature managementInternational Journal on Digital Libraries10.1007/s00799-023-00389-825:1(123-136)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00799-023-00389-8
Watanabe SIto HMatsubara MMorishima A(2022)Bibrecord-Based Literature Management with Interactive Latent Space LearningFrom Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries10.1007/978-3-031-21756-2_13(155-171)Online publication date: 7-Dec-2022
https://doi.org/10.1007/978-3-031-21756-2_13
Kaya MSchoop M(2021)Analytical Comparison of Clustering Techniques for the Recognition of Communication PatternsGroup Decision and Negotiation10.1007/s10726-021-09758-731:3(555-589)Online publication date: 13-Oct-2021
https://doi.org/10.1007/s10726-021-09758-7
Xu SXu DWen LZhu CYang YHan SGuan P(2020)
Integrating Unified Medical Language System and Kleinberg’s Burst Detection Algorithm into Research Topics of Medications for Post-Traumatic Stress Disorder
Drug Design, Development and Therapy10.2147/DDDT.S270379Volume 14(3899-3913)Online publication date: Sep-2020
https://doi.org/10.2147/DDDT.S270379
Li H(2018)Text ClusteringEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_415(4067-4070)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_415
Shah SLuo X(2017)Exploring diseases based biomedical document clustering and visualization using self-organizing maps2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)10.1109/HealthCom.2017.8210791(1-6)Online publication date: Oct-2017
https://doi.org/10.1109/HealthCom.2017.8210791
Cheng CWang M(2017)Healthcare Data Mining, Association Rule Mining, and ApplicationsHealth Informatics Data Analysis10.1007/978-3-319-44981-4_13(201-210)Online publication date: 10-Sep-2017
https://doi.org/10.1007/978-3-319-44981-4_13
Yea SSeong BJang YKim C(2016)A data mining approach to selecting herbs with similar efficacy: Targeted selection methods based on medical subject headings (MeSH)Journal of Ethnopharmacology10.1016/j.jep.2016.02.007182(27-34)Online publication date: Apr-2016
https://doi.org/10.1016/j.jep.2016.02.007
Li H(2016)Text ClusteringEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_415-2(1-4)Online publication date: 20-Dec-2016
https://doi.org/10.1007/978-1-4899-7993-3_415-2
Consoli SStilianakis N(2015)A quartet method based on variable neighborhood search for biomedical literature extraction and clusteringInternational Transactions in Operational Research10.1111/itor.1224024:3(537-558)Online publication date: 14-Dec-2015
https://doi.org/10.1111/itor.12240
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten