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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum, New York (1981)
Brivio, P.A., Binaghi, E., Rampini, A., Ghezzi, P.: A Fuzzy Set-Based Accuracy Assessment of Soft Classifications. Pattern Recognition Letters 20, 935–948 (1999)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. Int. Conf. Intell. Syst. Mol. Bio., vol. 8, pp. 93–103 (2000)
Congalton, R.G.: Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press, Boca Raton (1999)
Bousquet, O., Elisseeff, A.: Stability and generalization. J. Mach. Learn. Res. 2, 499–526 (2002)
Browder, F.: Nonexpansive nonlinear operators in a banach space. Proc. Nat. Acad. Sci. U.S.A. 54, 1041–1044 (1965)
Cho, H., Dhillon, I.S., Guan, Y., Sra, S.: Minimum Sum-Squared Residue Co-clustering of Gene Expression Data. In: Proc. Fourth SIAM Int. Conf. on Data Mining, pp. 114–125 (2004)
Filippone, M., Masulli, F., Rovetta, S., Mitra, S., Banka, H.: Possibilistic approach to biclustering: An application to oligonucleotide microarray data analysis. In: Priami, C. (ed.) CMSB 2006. LNCS (LNBI), vol. 4210, pp. 312–322. Springer, Heidelberg (2006)
Fred, A.L.N., Jain, A.K.: Robust Data Clustering. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), vol. 2, pp. 128–136 (2003)
Göhde, D.: Zum prinzip der kontraktiven abbildung. Math. Nachr. 30, 251–258 (1965)
Hartigan, J.A.: Direct clustering of a data matrix. Journal of the American Statistical Association 67(337), 123–129 (1972)
Jaccard, P.: Etude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)
Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1(2), 98–110 (1993)
Krishnapuram, R., Keller, J.M.: The possibilistic C-Means algorithm: insights and recommendations. IEEE Transactions on Fuzzy Systems 4(3), 385–393 (1996)
Kuncheva, L.I., Vetrov, D.P.: Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1798–1808 (2006)
Kung, S.Y., Mak, M.W., Tagkopoulos, I.: Multi-metric and multi-substructure biclustering analysis for gene expression data. In: Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference (CSB 2005) (2005)
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics 1, 24–45 (2004)
Masulli, F., Schenone, A.: A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artificial Intelligence in Medicine 16, 129–147 (1999)
Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recogn. 39(12), 2464–2477 (2006)
Monti, S., Tamayo, P., Mesirov, J., Golub, T.: Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning 52(1-2), 91–118 (2003)
Peeters, R.: The maximum edge biclique problem is NP-Complete. Discrete Applied Mathematics 131, 651–654 (2003)
Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecturem. Nature Genetics 22(3) (1999)
Tjhi, W.-C., Chen, L.: Minimum sum-squared residue for fuzzy co-clustering. Intelligent Data Analysis 10(3), 237–249 (2006)
Turner, H., Bailey, T., Krzanowski, W.: Improved biclustering of microarray data demonstrated through systematic performance tests. Computational Statistics and Data Analysis 48(2), 235–254 (2005)
Yang, J., Wang, H., Wang, W., Yu, P.: Enhanced biclustering on expression data. In: BIBE 2003: Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering, p. 321. IEEE Computer Society, Washington (2003)
Zhang, Z., Teo, A., Ooi, B.C.A.: Mining deterministic biclusters in gene expression data. In: Proceedings of the Fourth IEEE Symposium on Bioinformatics and Bioengineering (BIBE 2004), pp. 283–292 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Filippone, M., Masulli, F., Rovetta, S. (2009). Stability and Performances in Biclustering Algorithms. In: Masulli, F., Tagliaferri, R., Verkhivker, G.M. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2008. Lecture Notes in Computer Science(), vol 5488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02504-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-02504-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02503-7
Online ISBN: 978-3-642-02504-4
eBook Packages: Computer ScienceComputer Science (R0)