Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data

Kyung Joong Kim; Si Ho Yoo; Sung Bae Cho

doi:10.1007/11553939_110

Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data

Kyung Joong Kim, Si Ho Yoo, Sung Bae Cho

College of Computing

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

1 Citation (Scopus)

Abstract

Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions that are obtained by fuzzy clustering. In this paper, a Bayesian validation method of selecting a fuzzy partition with the largest posterior probability given the dataset is proposed to evaluate the fuzzy partitions effectively. Analysis of yeast cell-cycle data follows to show the usefulness of the proposed method.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Publisher	Springer Verlag
Pages	777-784
Number of pages	8
ISBN (Print)	3540288961, 9783540288961
DOIs	https://doi.org/10.1007/11553939_110
Publication status	Published - 2005
Event	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia Duration: 2005 Sept 14 → 2005 Sept 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3683 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Country/Territory	Australia
City	Melbourne
Period	05/9/14 → 05/9/16

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/11553939_110

Cite this

Kim, K. J., Yoo, S. H., & Cho, S. B. (2005). Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 777-784). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI). Springer Verlag. https://doi.org/10.1007/11553939_110

Kim, Kyung Joong ; Yoo, Si Ho ; Cho, Sung Bae. / Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. pp. 777-784 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data",

abstract = "Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions that are obtained by fuzzy clustering. In this paper, a Bayesian validation method of selecting a fuzzy partition with the largest posterior probability given the dataset is proposed to evaluate the fuzzy partitions effectively. Analysis of yeast cell-cycle data follows to show the usefulness of the proposed method.",

author = "Kim, {Kyung Joong} and Yoo, {Si Ho} and Cho, {Sung Bae}",

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

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Kim, KJ, Yoo, SH & Cho, SB 2005, Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. in Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3683 LNAI, Springer Verlag, pp. 777-784, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 05/9/14. https://doi.org/10.1007/11553939_110

Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. / Kim, Kyung Joong; Yoo, Si Ho; Cho, Sung Bae.

Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. p. 777-784 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

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

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N2 - Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions that are obtained by fuzzy clustering. In this paper, a Bayesian validation method of selecting a fuzzy partition with the largest posterior probability given the dataset is proposed to evaluate the fuzzy partitions effectively. Analysis of yeast cell-cycle data follows to show the usefulness of the proposed method.

AB - Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions that are obtained by fuzzy clustering. In this paper, a Bayesian validation method of selecting a fuzzy partition with the largest posterior probability given the dataset is proposed to evaluate the fuzzy partitions effectively. Analysis of yeast cell-cycle data follows to show the usefulness of the proposed method.

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M3 - Conference contribution

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

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Kim KJ, Yoo SH, Cho SB. Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag. 2005. p. 777-784. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11553939_110

Bayesian validation of fuzzy clustering for analysis of yeast cell cycle data

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