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DIVERSITY-BASED ATTRIBUTE WEIGHTING FOR K-MODES CLUSTERING

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 7 No. 2 (2014) · pp. 61–66

DOI
10.21609/jiki.v7i2.258
ISSN
2502-9274, 2088-7051
Published
2014-08-21
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Abstract Categorical data is a kind of data that is used for computational in computer science. To obtain the information from categorical data input, it needs a clustering algorithm. There are so many clustering algorithms that are given by the researchers. One of the clustering algorithms for categorical data is k-modes. K-modes uses a simple matching approach. This simple matching approach uses similarity values. In K-modes, the two similar objects have similarity value 1, and 0 if it is otherwise. Actually, in each attribute, there are some kinds of different attribute value and each kind of attribute value has different number. The similarity value 0 and 1 is not enough to represent the real semantic distance between a data object and a cluster. Thus in this paper, we generalize a k-modes algorithm for categorical data by adding the weight and diversity value of each attribute value to optimize categorical data clustering.

Keywords

Collaboration network of the authors

4 authors · 4 co-authorship links

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  • Institut Teknologi Sepuluh Nopember
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Co-authorOrganisationJoint articles
Waskitho WibisonoInstitut Teknologi Sepuluh Nopember1

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://jiki.cs.ui.ac.id/index.php/jiki/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">DIVERSITY-BASED ATTRIBUTE WEIGHTING FOR K-MODES CLUSTERING</dc:title>
	<dc:creator>Huda, Muhammad Misbachul</dc:creator>
	<dc:creator>Hayun, Dian Rahma</dc:creator>
	<dc:creator>Indarwanti, Annisaa Sri</dc:creator>
	<dc:subject xml:lang="en-US">categorical data</dc:subject>
	<dc:subject xml:lang="en-US">diversity</dc:subject>
	<dc:subject xml:lang="en-US">K-modes</dc:subject>
	<dc:subject xml:lang="en-US">attribute weighting.</dc:subject>
	<dc:description xml:lang="en-US">Abstract

Categorical data is a kind of data that is used for computational in computer science. To obtain the information from categorical data input, it needs a clustering algorithm. There are so many clustering algorithms that are given by the researchers. One of the clustering algorithms for categorical data is k-modes. K-modes uses a simple matching approach. This simple matching approach uses similarity values. In K-modes, the two similar objects have similarity value 1, and 0 if it is otherwise. Actually, in each attribute, there are some kinds of different attribute value and each kind of attribute value has different number. The similarity value 0 and 1 is not enough to represent the real semantic distance between a data object and a cluster. Thus in this paper, we generalize a k-modes algorithm for categorical data by adding the weight and diversity value of each attribute value to optimize categorical data clustering.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2014-08-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/258</dc:identifier>
	<dc:identifier>10.21609/jiki.v7i2.258</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 7 No. 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 61-66</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v7i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/258/316</dc:relation>
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