Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures

Kadim Taşdemir; Berna Yalçin; Isa Yildirim

Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures

Kadim Taşdemir^*, Berna Yalçin, Isa Yildirim

^*Corresponding author for this work

Antalya Bilim University

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

Abstract

This is a summary of the paper published in [1] which proposes new hybrid similarity measures exploiting various information types such as density, distance and topology, to achieve high accuracies by approximate spectral clustering (an algorithm based on similarity based graph-cut optimization). The experiments in [1] on a wide variety of datasets show the outperformance of the proposed advanced similarities.

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	226-228
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

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Keywords

Approximate spectral clustering
Geodesic distances
Hybrid similarity measures
Manifold learning

Cite this

Taşdemir, K., Yalçin, B., & Yildirim, I. (2015). Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures. In M. Pelillo, M. Loog, & A. Feragen (Eds.), Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings (pp. 226-228). (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 ; Yildirim, Isa. / Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures. Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings. editor / Marcello Pelillo ; Marco Loog ; Aasa Feragen. Springer Verlag, 2015. pp. 226-228 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2e9ad22c85a846c1b64cb836e5ee1a98,

title = "Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures",

abstract = "This is a summary of the paper published in [1] which proposes new hybrid similarity measures exploiting various information types such as density, distance and topology, to achieve high accuracies by approximate spectral clustering (an algorithm based on similarity based graph-cut optimization). The experiments in [1] on a wide variety of datasets show the outperformance of the proposed advanced similarities.",

keywords = "Approximate spectral clustering, Geodesic distances, Hybrid similarity measures, Manifold learning",

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

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015 ; Conference date: 12-10-2015 Through 14-10-2015",

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 = "226--228",

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

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

address = "Germany",

}

Taşdemir, K, Yalçin, B & Yildirim, I 2015, Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures. 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. 226-228, 3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015, Copenhagen, Denmark, 12/10/15.

Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures. / Taşdemir, Kadim; Yalçin, Berna ; Yildirim, Isa.
Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings. ed. / Marcello Pelillo; Marco Loog; Aasa Feragen. Springer Verlag, 2015. p. 226-228 (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

TY - GEN

T1 - Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures

AU - Taşdemir, Kadim

AU - Yalçin, Berna

AU - Yildirim, Isa

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - This is a summary of the paper published in [1] which proposes new hybrid similarity measures exploiting various information types such as density, distance and topology, to achieve high accuracies by approximate spectral clustering (an algorithm based on similarity based graph-cut optimization). The experiments in [1] on a wide variety of datasets show the outperformance of the proposed advanced similarities.

AB - This is a summary of the paper published in [1] which proposes new hybrid similarity measures exploiting various information types such as density, distance and topology, to achieve high accuracies by approximate spectral clustering (an algorithm based on similarity based graph-cut optimization). The experiments in [1] on a wide variety of datasets show the outperformance of the proposed advanced similarities.

KW - Approximate spectral clustering

KW - Geodesic distances

KW - Hybrid similarity measures

KW - Manifold learning

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M3 - Conference contribution

AN - SCOPUS:84945921436

SN - 9783319242606

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 226

EP - 228

BT - Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings

A2 - Pelillo, Marcello

A2 - Loog, Marco

A2 - Feragen, Aasa

PB - Springer Verlag

T2 - 3rd International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2015

Y2 - 12 October 2015 through 14 October 2015

ER -

Taşdemir K, Yalçin B , Yildirim I. Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures. In Pelillo M, Loog M, Feragen A, editors, Similarity-Based Pattern Recognition - 3rd International Workshop, SIMBAD 2015, Proceedings. Springer Verlag. 2015. p. 226-228. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures

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