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Document Grouping by Using Meronyms and Type-2 Fuzzy Association Rule Mining

Journal of ICT Research and Applications (JICTRA) · Vol. 11 No. 3 (2017) · pp. 268–283

DOI
10.5614/itbj.ict.res.appl.2017.11.3.4
ISSN
2338-5499, 2337-5787
Published
2017-12-22
Publisher
DRI - ITB
Language
eng

Abstract

The growth of the number of textual documents in the digital world, especially on the World Wide Web, is incredibly fast. This causes an accumulation of information, so we need efficient organization to manage textual documents. One way to accurately classify documents is using fuzzy association rules. The quality of the document clustering is affected by phase extraction of key terms and type of fuzzy logic system (FLS) used for clustering. The use of meronyms in the extraction of key terms to obtain cluster labels helps obtaining meaningful cluster labels and in addition ambiguities and uncertainties that occur in the rules of type-1 fuzzy logic systems can be overcome by using type-2 fuzzy sets. This study proposes a method of key term extraction based on meronyms with an initialization cluster using fuzzy association rule mining for document clustering. This method consists of four stages, i.e. preprocessing of the document, extraction of key terms with meronyms, extraction of candidate clusters, and cluster tree construction. Testing of this method was done with three different datasets: classic, Reuters, and 20 Newsgroup. Testing was done by comparing the overall F-measure of the method without meronyms and with meronyms. Based on the testing, the method with meronyms in the extraction of keywords produced an overall F-measure of 0.5753 for the classic dataset, 0.3984 for the Reuters dataset, and 0.6285 for the 20 Newsgroup dataset.

Keywords

Collaboration network of the authors

40 authors · 86 co-authorship links · 13 across different organisations · showing the 40 most connected

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  • Institut Teknologi Sepuluh Nopember
  • University of Brawijaya
  • Universitas Muhammadiyah Malang
  • Universitas Muhammadiyah Gresik
  • State University of Semarang
  • Institut Ilmu Sosial dan Manajemen STIAMI
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhsin MuhsinAffiliation unknown4
Diana PurwitasariInstitut Teknologi Sepuluh Nopember2
Satrio Hadi WijoyoUniversity of Brawijaya2
Septiyan Andika IsantaUniversitas Muhammadiyah Malang2
Niken SubektiState University of Semarang2
Heru SetyantoAffiliation unknown2
Sonika MaulanaAffiliation unknown2
Rifa’atunnisa Rifa’atunnisaAffiliation unknown2
Juhadi JuhadiAffiliation unknown2
Hendi PratamaAffiliation unknown2
Dwi Anggani Linggar BharatiAffiliation unknown2
Abdurrahman FaridiAffiliation unknown2
Joko WidodoAffiliation unknown2
Heri YantoAffiliation unknown2
Rusdarti RusdartiAffiliation unknown2
Chastine FatichahInstitut Teknologi Sepuluh Nopember1
Yufis AzharUniversitas Muhammadiyah Malang1
Admaja Dwi HerlambangUniversity of Brawijaya1
Amelia WidihastutikAffiliation unknown1
Yohana SetyaniAffiliation unknown1
Atun Sri RejekiAffiliation unknown1
Yulianti YuliantiInstitut Ilmu Sosial dan Manajemen STIAMI1
Baruna Rizky PerdanaAffiliation unknown1
Danang Eko SaputroAffiliation unknown1
Puji AstutiAffiliation unknown1
Ali MasyharAffiliation unknown1
Zulfa SakhiyyaAffiliation unknown1
Heru SaputraAffiliation unknown1
Dolendra PaudelAffiliation unknown1
Sugeng RomadhoniAffiliation unknown1
Apriani Kartika SariAffiliation unknown1
Dewanti Citra WijayantiAffiliation unknown1
Eva Dwi LAffiliation unknown1
Nanik SuryaniAffiliation unknown1
Ayuni Tri HaryantiAffiliation unknown1
Nina OktarinaAffiliation unknown1
Ayu Widi PangestiAffiliation unknown1
Eva Nur RahayuAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Document Grouping by Using Meronyms and Type-2 Fuzzy Association Rule Mining</dc:title>
	<dc:creator>Rozi, Fahrur</dc:creator>
	<dc:creator>Sukmana, Farid</dc:creator>
	<dc:subject xml:lang="en-US">association rule</dc:subject>
	<dc:subject xml:lang="en-US">document clustering</dc:subject>
	<dc:subject xml:lang="en-US">type-2 fuzzy sets</dc:subject>
	<dc:subject xml:lang="en-US">meronyms</dc:subject>
	<dc:subject xml:lang="en-US">overall F-measures</dc:subject>
	<dc:subject xml:lang="en-US">textual documents.</dc:subject>
	<dc:description xml:lang="en-US">The growth of the number of textual documents in the digital world, especially on the World Wide Web, is incredibly fast. This causes an accumulation of information, so we need efficient organization to manage textual documents. One way to accurately classify documents is using fuzzy association rules. The quality of the document clustering is affected by phase extraction of key terms and type of fuzzy logic system (FLS) used for clustering. The use of meronyms in the extraction of key terms to obtain cluster labels helps obtaining meaningful cluster labels and in addition ambiguities and uncertainties that occur in the rules of type-1 fuzzy logic systems can be overcome by using type-2 fuzzy sets. This study proposes a method of key term extraction based on meronyms with an initialization cluster using fuzzy association rule mining for document clustering. This method consists of four stages, i.e. preprocessing of the document, extraction of key terms with meronyms, extraction of candidate clusters, and cluster tree construction. Testing of this method was done with three different datasets: classic, Reuters, and 20 Newsgroup. Testing was done by comparing the overall F-measure of the method without meronyms and with meronyms. Based on the testing, the method with meronyms in the extraction of keywords produced an overall F-measure of 0.5753 for the classic dataset, 0.3984 for the Reuters dataset, and 0.6285 for the 20 Newsgroup dataset.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2017-12-22</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/2367</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2017.11.3.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 11 No. 3 (2017); 268-283</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/2367/3047</dc:relation>
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