Fuzzy clustering of time series gene expression data with cubic-splineReport as inadecuate




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Data clusteringtechniques have been applied to ex- tractinformation from gene expression data for two decades. A large volume of novelclustering algorithmshave been developed and achieved great success.However, due to the diverse structures and intensivenoise, there is no reliable clustering approach can be applied to all geneexpression data. In this paper, weaim to the feature of high noise and propose a cubic smoothing spline fittedfor the time course ex- pressionprofile, by which noise can be filtered and then groups genes into clusters byapplying fuzzy c-means clusteringon the resulting splines FCMS. The discrete values of radius of curvature areused to compute the similarity between spline curves. Results on geneexpression data show that the FCMS has better performance than the originalfuzzy c-means on reliability and noise robustness.

KEYWORDS

Fuzzy c-Means; Cubic Spline; Noise; Radius of Curvature

Cite this paper

Wang, Y. , Angelova, M. and Ali, A. 2013 Fuzzy clustering of time series gene expression data with cubic-spline. Journal of Biosciences and Medicines, 1, 16-21. doi: 10.4236-jbm.2013.13004.





Author: Yu Wang, Maia Angelova, Akhtar Ali

Source: http://www.scirp.org/



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