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A Multivariate Fuzzy Weighted K-Modes Algorithm with Probabilistic Distance for Categorical Data

Journal of ICT Research and Applications (JICTRA) · Vol. 18 No. 2 (2024) · pp. 93–107

DOI
10.5614/itbj.ict.res.appl.2023.18.2.2
ISSN
2338-5499, 2337-5787
Published
2024-09-30
Publisher
DRI - ITB
Language
eng

Abstract

Data clustering is a data mining approach that assigns similar data to the same group. Traditionally, cluster similarity considers all attributes equally, but in real-world applications, some attributes may be more important than others. Therefore, this study proposes an algorithm that utilizes multivariate fuzzy weighting to demonstrate the varying importance of each attribute, using a Gini impurity measure for weight assignment. Additionally, the proposed algorithm implements probabilistic distance to reduce sensitivity to noise. Probabilistic distance offers more detailed information and better interpretation than Hamming distance, which ignores attribute positions. Probabilistic distance utilizes information about the attribute’s position within and between clusters. This enhances clustering performance by creating clusters with more similar attributes. Therefore, the proposed Multivariate Fuzzy Weighted K-Modes with Probabilistic Distance for Categorical Data (MFWKM-PD) algorithm, based on the multivariate fuzzy K-modes algorithm, not only considers detailed membership calculations but also considers the varying contributions of attributes and their positions in distance calculation. This study evaluated the proposed MFWKM-PD using several benchmark datasets. The experiments validated that the proposed MFWKM-PD shows promising results compared to other algorithms in terms of accuracy, NMI, and ARI.

Keywords

Collaboration network of the authors

5 authors · 7 co-authorship links · 4 across different organisations

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  • National Taiwan University of Science and Technology
  • University of Da Nang
  • Universitas Gadjah Mada
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Azhari S.N.Universitas Gadjah Mada1

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 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A Multivariate Fuzzy Weighted K-Modes Algorithm with Probabilistic Distance for Categorical Data</dc:title>
	<dc:creator>Kuo, Ren-Jieh</dc:creator>
	<dc:creator>Cendana, Maya</dc:creator>
	<dc:creator>Nguyen, Thi Phuong Quyen</dc:creator>
	<dc:creator>Zulvia, Ferani E.</dc:creator>
	<dc:subject xml:lang="en-US">categorical data</dc:subject>
	<dc:subject xml:lang="en-US">fuzzy clustering</dc:subject>
	<dc:subject xml:lang="en-US">gini impurity</dc:subject>
	<dc:subject xml:lang="en-US">MFWKM-PD</dc:subject>
	<dc:subject xml:lang="en-US">probabilistic distance</dc:subject>
	<dc:description xml:lang="en-US">Data clustering is a data mining approach that assigns similar data to the same group. Traditionally, cluster similarity considers all attributes equally, but in real-world applications, some attributes may be more important than others. Therefore, this study proposes an algorithm that utilizes multivariate fuzzy weighting to demonstrate the varying importance of each attribute, using a Gini impurity measure for weight assignment. Additionally, the proposed algorithm implements probabilistic distance to reduce sensitivity to noise. Probabilistic distance offers more detailed information and better interpretation than Hamming distance, which ignores attribute positions. Probabilistic distance utilizes information about the attribute’s position within and between clusters. This enhances clustering performance by creating clusters with more similar attributes. Therefore, the proposed Multivariate Fuzzy Weighted K-Modes with Probabilistic Distance for Categorical Data (MFWKM-PD) algorithm, based on the multivariate fuzzy K-modes algorithm, not only considers detailed membership calculations but also considers the varying contributions of attributes and their positions in distance calculation. This study evaluated the proposed MFWKM-PD using several benchmark datasets. The experiments validated that the proposed MFWKM-PD shows promising results compared to other algorithms in terms of accuracy, NMI, and ARI.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2024-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<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/23258</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2023.18.2.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 18 No. 2 (2024); 93-107</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2023.18.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/23258/6768</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Journal of ICT Research and Applications</dc:rights>
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