Can the optimum similarity matrix be selected before clustering for graph-based approaches?

Kadim Taşdemir; Berna Yalçin

Can the optimum similarity matrix be selected before clustering for graph-based approaches?

^*Corresponding author for this work

Antalya Bilim University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Original language	English
Title of host publication	Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings
Editors	Marcello Pelillo, Marco Loog, Aasa Feragen
Publisher	Springer Verlag
Pages	223-225
Number of pages	3
ISBN (Print)	9783319242606
Publication status	Published - 2015
Externally published	Yes
Event	3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015 - Copenhagen, Denmark Duration: 12 Oct 2015 → 14 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9370
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015
Country/Territory	Denmark
City	Copenhagen
Period	12/10/15 → 14/10/15

Keywords

Approximate spectral clustering
Assortativity
Clustering coefficient
Graph-based evaluation measures
Similarity measures

Cite this

Taşdemir, K., & Yalçin, B. (2015). Can the optimum similarity matrix be selected before clustering for graph-based approaches? In M. Pelillo, M. Loog, & A. Feragen (Eds.), Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings (pp. 223-225). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9370). Springer Verlag.

Taşdemir, Kadim ; Yalçin, Berna. / Can the optimum similarity matrix be selected before clustering for graph-based approaches?. Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings. editor / Marcello Pelillo ; Marco Loog ; Aasa Feragen. Springer Verlag, 2015. pp. 223-225 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Can the optimum similarity matrix be selected before clustering for graph-based approaches?",

keywords = "Approximate spectral clustering, Assortativity, Clustering coefficient, Graph-based evaluation measures, Similarity measures",

author = "Kadim Ta{\c s}demir and Berna Yal{\c c}in",

year = "2015",

language = "English",

isbn = "9783319242606",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "223--225",

editor = "Marcello Pelillo and Marco Loog and Aasa Feragen",

booktitle = "Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings",

address = "Germany",

note = "3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015 ; Conference date: 12-10-2015 Through 14-10-2015",

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Taşdemir, K & Yalçin, B 2015, Can the optimum similarity matrix be selected before clustering for graph-based approaches? in M Pelillo, M Loog & A Feragen (eds), Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9370, Springer Verlag, pp. 223-225, 3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015, Copenhagen, Denmark, 12/10/15.

Can the optimum similarity matrix be selected before clustering for graph-based approaches? / Taşdemir, Kadim; Yalçin, Berna.
Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings. ed. / Marcello Pelillo; Marco Loog; Aasa Feragen. Springer Verlag, 2015. p. 223-225 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9370).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review