Stability and performances in biclustering algorithms

Maurizio Filippone; Francesco Masulli; Stefano Rovetta

doi:10.1007/978-3-642-02504-4_8

Stability and performances in biclustering algorithms

Maurizio Filippone^*, Francesco Masulli, Stefano Rovetta

^*Corresponding author for this work

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

7 Scopus citations

Abstract

Stability is an important property of machine learning algorithms. Stability in clustering may be related to clustering quality or ensemble diversity, and therefore used in several ways to achieve a deeper understanding or better confidence in bioinformatic data analysis. In the specific field of fuzzy biclustering, stability can be analyzed by porting the definition of existing stability indexes to a fuzzy setting, and then adapting them to the biclustering problem. This paper presents work done in this direction, by selecting some representative stability indexes and experimentally verifying and comparing their properties. Experimental results are presented that indicate both a general agreement and some differences among the selected methods.

Original language	English (US)
Title of host publication	Computational Intelligence Methods for Bioinformatics and Biostatistics - 5th International Meeting, CIBB 2008, Revised Selected Papers
Pages	91-101
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-642-02504-4_8
State	Published - 2009
Event	5th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2008 - Vietri sul Mare, Italy Duration: Oct 3 2008 → Oct 4 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5488 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2008
Country/Territory	Italy
City	Vietri sul Mare
Period	10/3/08 → 10/4/08

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-02504-4_8

Cite this

Filippone, M., Masulli, F., & Rovetta, S. (2009). Stability and performances in biclustering algorithms. In Computational Intelligence Methods for Bioinformatics and Biostatistics - 5th International Meeting, CIBB 2008, Revised Selected Papers (pp. 91-101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5488 LNBI). https://doi.org/10.1007/978-3-642-02504-4_8

Filippone, Maurizio ; Masulli, Francesco ; Rovetta, Stefano. / Stability and performances in biclustering algorithms. Computational Intelligence Methods for Bioinformatics and Biostatistics - 5th International Meeting, CIBB 2008, Revised Selected Papers. 2009. pp. 91-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Stability and performances in biclustering algorithms",

abstract = "Stability is an important property of machine learning algorithms. Stability in clustering may be related to clustering quality or ensemble diversity, and therefore used in several ways to achieve a deeper understanding or better confidence in bioinformatic data analysis. In the specific field of fuzzy biclustering, stability can be analyzed by porting the definition of existing stability indexes to a fuzzy setting, and then adapting them to the biclustering problem. This paper presents work done in this direction, by selecting some representative stability indexes and experimentally verifying and comparing their properties. Experimental results are presented that indicate both a general agreement and some differences among the selected methods.",

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Filippone, M, Masulli, F & Rovetta, S 2009, Stability and performances in biclustering algorithms. in Computational Intelligence Methods for Bioinformatics and Biostatistics - 5th International Meeting, CIBB 2008, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5488 LNBI, pp. 91-101, 5th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2008, Vietri sul Mare, Italy, 10/3/08. https://doi.org/10.1007/978-3-642-02504-4_8

Stability and performances in biclustering algorithms. / Filippone, Maurizio; Masulli, Francesco; Rovetta, Stefano.
Computational Intelligence Methods for Bioinformatics and Biostatistics - 5th International Meeting, CIBB 2008, Revised Selected Papers. 2009. p. 91-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5488 LNBI).

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

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AB - Stability is an important property of machine learning algorithms. Stability in clustering may be related to clustering quality or ensemble diversity, and therefore used in several ways to achieve a deeper understanding or better confidence in bioinformatic data analysis. In the specific field of fuzzy biclustering, stability can be analyzed by porting the definition of existing stability indexes to a fuzzy setting, and then adapting them to the biclustering problem. This paper presents work done in this direction, by selecting some representative stability indexes and experimentally verifying and comparing their properties. Experimental results are presented that indicate both a general agreement and some differences among the selected methods.

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Filippone M, Masulli F, Rovetta S. Stability and performances in biclustering algorithms. In Computational Intelligence Methods for Bioinformatics and Biostatistics - 5th International Meeting, CIBB 2008, Revised Selected Papers. 2009. p. 91-101. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-02504-4_8

Stability and performances in biclustering algorithms

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