Article

Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature clustering

Authors:

Il-Yeol SongAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 791 - 796

https://doi.org/10.1145/1150402.1150505

Published: 20 August 2006 Publication History

Abstract

We introduce a novel document clustering approach that overcomes those problems by combining a semantic-based bipartite graph representation and a mutual refinement strategy. The primary contributions of this paper are the following. First, we introduce a new representation of documents using a bipartite graph between documents and co-occurrence concepts in the documents. Second, we show how to enhance clustering quality by applying the mutual refinement strategy to the initial clustering results. Third, through the experiments on MEDLINE documents, we show that our integrated method significantly enhances cluster quality and clustering reliability compared to existing clustering methods. Our approach improves on the average 29.5 cluster quality and 26.3 clustering reliability, in terms of misclassification index, over Bisecting K-means with the best parameters.

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Cited By

Rafi MKhan HNadeem HShakeel H(2021)Unsupervised Topic Aware Document-Level Semantic Representation for Document Clustering2021 22nd International Arab Conference on Information Technology (ACIT)10.1109/ACIT53391.2021.9677217(1-10)Online publication date: 21-Dec-2021
https://doi.org/10.1109/ACIT53391.2021.9677217
Sandhiya RSundarambal M(2018)Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applicationsCluster Computing10.1007/s10586-018-2023-4Online publication date: 20-Mar-2018
https://doi.org/10.1007/s10586-018-2023-4
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
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Index Terms

Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature 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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Published In

cover image ACM Conferences

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

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]

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Publication History

Published: 20 August 2006

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KDD06

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Rafi MKhan HNadeem HShakeel H(2021)Unsupervised Topic Aware Document-Level Semantic Representation for Document Clustering2021 22nd International Arab Conference on Information Technology (ACIT)10.1109/ACIT53391.2021.9677217(1-10)Online publication date: 21-Dec-2021
https://doi.org/10.1109/ACIT53391.2021.9677217
Sandhiya RSundarambal M(2018)Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applicationsCluster Computing10.1007/s10586-018-2023-4Online publication date: 20-Mar-2018
https://doi.org/10.1007/s10586-018-2023-4
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
Abdelli BKazar OPinon J(2015)The impact of titles expansion based on ontology in document retrievalInternational Journal of Metadata, Semantics and Ontologies10.1504/IJMSO.2015.07387510:3(170-181)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1504/IJMSO.2015.073875
Dobrynin VBalykina YKamalov M(2015)Analysis of standard clustering algorithms for grouping MEDLINE abstracts into evidence-based medicine intervention categories2015 International Conference "Stability and Control Processes" in Memory of V.I. Zubov (SCP)10.1109/SCP.2015.7342223(555-557)Online publication date: Oct-2015
https://doi.org/10.1109/SCP.2015.7342223
Bing LJiang SLam WZhang YJameel S(2015)Adaptive Concept Resolution for document representation and its applications in text miningKnowledge-Based Systems10.1016/j.knosys.2014.10.00374:1(1-13)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.knosys.2014.10.003
Hípola PA. Senso JLeiva-Mederos ADomínguez-Velasco S(2014)Ontology-based text summarization. The case of TexminerLibrary Hi Tech10.1108/LHT-01-2014-000532:2(229-248)Online publication date: 10-Jun-2014
https://doi.org/10.1108/LHT-01-2014-0005
Davoodi EKianmehr KAfsharchi M(2013)A semantic social network-based expert recommender systemApplied Intelligence10.1007/s10489-012-0389-139:1(1-13)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1007/s10489-012-0389-1
Tang JWang XGao HHu XLiu H(2012)Enriching short text representation in microblog for clusteringFrontiers of Computer Science in China10.5555/2125163.21251896:1(88-101)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.5555/2125163.2125189
Tang JWang XGao HHu XLiu H(2012)Enriching short text representation in microblog for clusteringFrontiers of Computer Science10.1007/s11704-011-1167-76:1(88-101)Online publication date: 27-Jan-2012
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