Comparing clustering algorithms based on structural similarity

Nassim Sohaee; Christian V. Forst

doi:10.2316/P.2011.753-029

Comparing clustering algorithms based on structural similarity

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

Abstract

In this paper we will introduce a new metric to compare two biological clusters. This metric measures both structural and node similarity of two clusters. We replace Neighborhood Affinity function by this metric and show that some of the previously known matched clusters are showing a very low structural similarity.

Original language	English
Title of host publication	Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011
Pages	122-125
Number of pages	4
DOIs	https://doi.org/10.2316/P.2011.753-029
State	Published - 2011
Externally published	Yes
Event	6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011 - Pittsburgh, PA, United States Duration: 7 Nov 2011 → 9 Nov 2011

Publication series

Name	Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011

Conference

Conference	6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011
Country/Territory	United States
City	Pittsburgh, PA
Period	7/11/11 → 9/11/11

Keywords

Affinity
Clustering
Hamming
PPI network

Access to Document

10.2316/P.2011.753-029

Cite this

Sohaee, N., & Forst, C. V. (2011). Comparing clustering algorithms based on structural similarity. In Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011 (pp. 122-125). (Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011). https://doi.org/10.2316/P.2011.753-029

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title = "Comparing clustering algorithms based on structural similarity",

abstract = "In this paper we will introduce a new metric to compare two biological clusters. This metric measures both structural and node similarity of two clusters. We replace Neighborhood Affinity function by this metric and show that some of the previously known matched clusters are showing a very low structural similarity.",

keywords = "Affinity, Clustering, Hamming, PPI network",

author = "Nassim Sohaee and Forst, {Christian V.}",

year = "2011",

doi = "10.2316/P.2011.753-029",

language = "English",

isbn = "9780889869042",

series = "Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011",

pages = "122--125",

booktitle = "Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011",

note = "6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011 ; Conference date: 07-11-2011 Through 09-11-2011",

}

Sohaee, N & Forst, CV 2011, Comparing clustering algorithms based on structural similarity. in Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011. Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011, pp. 122-125, 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011, Pittsburgh, PA, United States, 7/11/11. https://doi.org/10.2316/P.2011.753-029

Comparing clustering algorithms based on structural similarity. / Sohaee, Nassim; Forst, Christian V.
Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011. 2011. p. 122-125 (Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011).

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

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AU - Forst, Christian V.

PY - 2011

Y1 - 2011

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AB - In this paper we will introduce a new metric to compare two biological clusters. This metric measures both structural and node similarity of two clusters. We replace Neighborhood Affinity function by this metric and show that some of the previously known matched clusters are showing a very low structural similarity.

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KW - Clustering

KW - Hamming

KW - PPI network

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U2 - 10.2316/P.2011.753-029

DO - 10.2316/P.2011.753-029

M3 - Conference contribution

AN - SCOPUS:84856657382

SN - 9780889869042

T3 - Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011

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BT - Proceedings of the 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011

T2 - 6th IASTED International Conference on Computational Intelligence and Bioinformatics, CIB 2011

Y2 - 7 November 2011 through 9 November 2011

ER -

Comparing clustering algorithms based on structural similarity

Abstract

Publication series

Conference

Keywords

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