Performance Evaluation of Cheng & Church (CC) and Spectral Biclustering Algorithms under Collinearity and Overlap Conditions

Hafsah, Siti; Indahwati, Indahwati; Wijayanto, Hari

doi:10.15294/sji.v12i2.26413

Journal

Performance Evaluation of Cheng & Church (CC) and Spectral Biclustering Algorithms under Collinearity and Overlap Conditions

Scientific Journal of Informatics · Vol. 12 No. 2 (2025) · pp. 371–386

DOI: 10.15294/sji.v12i2.26413
ISSN: 2407-7658, 2460-0040
Published: 2025-07-20
Publisher: Universitas Negeri Semarang
Language: eng

Abstract

Purpose: This study aims to address methodological challenges in evaluating biclustering algorithms under simultaneous collinearity and overlap, which often co-occur in real world multivariate data but are rarely analyzed simultaneously. This research highlights the importance of understanding how these structural challenges affect local pattern detection in data mining applications. Methods: A simulation study was conducted using synthetic matrices embedded with two constant biclusters under 15 combinations of collinearity levels (ρ = 0.3,0.6,0.9) and overlap degrees (none, small, large). Each scenario was replicated 100 times. Performance was assessed using the Liu and Wang Index (ILW), while a three-way ANOVA tested the effects of algorithm type, collinearity, and overlap. Result: Spectral Biclustering maintained stable ILW scores despite increasing collinearity, while CC performed better in low-overlap scenarios but was more sensitive to collinearity. Under high collinearity and large overlap, both algorithms experienced notable degradation. The ANOVA confirmed all main effects and interactions were significant (p < 0.001). Novelty: This study contributes empirical evidence regarding the influence of interacting structural characteristics on biclustering performance. The results deliver practical insights for selecting suitable algorithms and emphasize the potential advantages of hybrid approaches that integrate the stability of spectral methods with the adaptability of residual-based techniques.

Keywords

Collaboration network of the authors

12 authors · 18 co-authorship links

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Universitas Balikpapan
IPB University
Affiliation unknown

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Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Anwar Fitrianto	Affiliation unknown	2
Muhammad Adam	Universitas Balikpapan	1
Mohamad Solehudin Zaenal	Affiliation unknown	1
Farit Mochamad Afendi	Affiliation unknown	1
Nurzatil Aqmar	Affiliation unknown	1
Ahmad Syauqi	Affiliation unknown	1
Khairil Anwar Notodiputro	IPB University	1
Kayla Fakhriyya Jasmine	Affiliation unknown	1
Wandee Wanishsakpong	Affiliation unknown	1

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://journal.unnes.ac.id/journals/sji/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en">Performance Evaluation of Cheng &amp; Church (CC) and Spectral Biclustering Algorithms under Collinearity and Overlap Conditions</dc:title>
	<dc:creator xml:lang="en">Hafsah, Siti</dc:creator>
	<dc:creator xml:lang="en">Indahwati, Indahwati</dc:creator>
	<dc:creator xml:lang="en">Wijayanto,  Hari </dc:creator>
	<dc:subject xml:lang="en">Cheng &amp; Church Biclustering</dc:subject>
	<dc:subject xml:lang="en">Spectral Biclustering</dc:subject>
	<dc:subject xml:lang="en">Collinearity</dc:subject>
	<dc:subject xml:lang="en">Overlap</dc:subject>
	<dc:subject xml:lang="en">Liu and Wang Index</dc:subject>
	<dc:description xml:lang="en">Purpose: This study aims to address methodological challenges in evaluating biclustering algorithms under simultaneous collinearity and overlap, which often co-occur in real world multivariate data but are rarely analyzed simultaneously. This research highlights the importance of understanding how these structural challenges affect local pattern detection in data mining applications.
Methods: A simulation study was conducted using synthetic matrices embedded with two constant biclusters under 15 combinations of collinearity levels (ρ = 0.3,0.6,0.9) and overlap degrees (none, small, large). Each scenario was replicated 100 times. Performance was assessed using the Liu and Wang Index (ILW), while a three-way ANOVA tested the effects of algorithm type, collinearity, and overlap.
Result: Spectral Biclustering maintained stable ILW scores despite increasing collinearity, while CC performed better in low-overlap scenarios but was more sensitive to collinearity. Under high collinearity and large overlap, both algorithms experienced notable degradation. The ANOVA confirmed all main effects and interactions were significant (p &amp;lt; 0.001).
Novelty: This study contributes empirical evidence regarding the influence of interacting structural characteristics on biclustering performance. The results deliver practical insights for selecting suitable algorithms and emphasize the potential advantages of hybrid approaches that integrate the stability of spectral methods with the adaptability of residual-based techniques.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-07-20</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/26413</dc:identifier>
	<dc:identifier>10.15294/sji.v12i2.26413</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 12 No. 2: Regular Issue; 371-386</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v12i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/26413/5020</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Scientific Journal of Informatics</dc:rights>
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