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techniques have been applied to ex- tract
information from gene expression data for two decades. A large volume of novel
have been developed and achieved great success.
However, due to the diverse structures and intensive
noise, there is no reliable clustering approach can be applied to all gene
expression data. In this paper, we
aim to the feature of high noise and propose a cubic smoothing spline fitted
for the time course ex- pression
profile, by which noise can be filtered and then groups genes into clusters by
applying fuzzy c-means clustering
on the resulting splines (FCMS). The discrete values of radius of curvature are
used to compute the similarity between spline curves. Results on gene
expression data show that the FCMS has better performance than the original
fuzzy c-means on reliability and noise robustness.