Stability-based cluster analysis applied to microarray data

Ciprian Doru Giurcǎneanu; Ioan Tabus; Ilyu Shmulevich; Wei Zhang

doi:10.1109/ISSPA.2003.1224814

Stability-based cluster analysis applied to microarray data

Ciprian Doru Giurcǎneanu, Ioan Tabus, Ilyu Shmulevich, Wei Zhang

Pathology

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

10 Scopus citations

Abstract

This paper studies the estimation of the number of clusters using the so-called stability-based approach, where clusters obtained for two subsets of the dataset are compared via a similarity index and the decision regarding the number of clusters is taken based on the statistics of the index over randomly selected subsets. We introduce a new similarity index s(·,·) and analyze the consistency of the estimator of the number of classes when k-means algorithm is used in conjunction with s(·,·). Various similarity indices are experimentally evaluated when comparing the "true" data partition with the partition obtained at each level of a hierarchical clustering tree. Finally, experimental results with real data are reported for a glioma microarray dataset.

Original language	English (US)
Title of host publication	Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
Publisher	IEEE Computer Society
Pages	57-60
Number of pages	4
ISBN (Print)	0780379462, 9780780379466
DOIs	https://doi.org/10.1109/ISSPA.2003.1224814
State	Published - 2003
Event	7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 - Paris, France Duration: Jul 1 2003 → Jul 4 2003

Publication series

Name	Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
Volume	2

Other

Other	7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
Country/Territory	France
City	Paris
Period	7/1/03 → 7/4/03

ASJC Scopus subject areas

Signal Processing

Access to Document

10.1109/ISSPA.2003.1224814

Cite this

Giurcǎneanu, C. D., Tabus, I., Shmulevich, I., & Zhang, W. (2003). Stability-based cluster analysis applied to microarray data. In Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 (pp. 57-60). Article 1224814 (Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/ISSPA.2003.1224814

Stability-based cluster analysis applied to microarray data. / Giurcǎneanu, Ciprian Doru; Tabus, Ioan; Shmulevich, Ilyu et al.
Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003. IEEE Computer Society, 2003. p. 57-60 1224814 (Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003; Vol. 2).

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

Giurcǎneanu, CD, Tabus, I, Shmulevich, I & Zhang, W 2003, Stability-based cluster analysis applied to microarray data. in Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003., 1224814, Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003, vol. 2, IEEE Computer Society, pp. 57-60, 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003, Paris, France, 7/1/03. https://doi.org/10.1109/ISSPA.2003.1224814

Giurcǎneanu CD, Tabus I, Shmulevich I, Zhang W. Stability-based cluster analysis applied to microarray data. In Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003. IEEE Computer Society. 2003. p. 57-60. 1224814. (Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003). doi: 10.1109/ISSPA.2003.1224814

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AB - This paper studies the estimation of the number of clusters using the so-called stability-based approach, where clusters obtained for two subsets of the dataset are compared via a similarity index and the decision regarding the number of clusters is taken based on the statistics of the index over randomly selected subsets. We introduce a new similarity index s(·,·) and analyze the consistency of the estimator of the number of classes when k-means algorithm is used in conjunction with s(·,·). Various similarity indices are experimentally evaluated when comparing the "true" data partition with the partition obtained at each level of a hierarchical clustering tree. Finally, experimental results with real data are reported for a glioma microarray dataset.

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Stability-based cluster analysis applied to microarray data

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