Semi-supervised clustering algorithms aim at discovering the hidden structure of data sets with the help of expert knowledge, generally expressed as constraints on the data such as class labels or pairwise relations. Most of the time, the expert is considered as an oracle that only provides correct constraints. This paper focuses on the case where some label constraints are erroneous and proposes to investigate into more detail three semi-supervised fuzzy c-means clustering approaches as they have been tailored to naturally handle uncertainty in the expert labeling. In order to run a fair comparison between existing algorithms, formal improvements have been proposed to guarantee and fasten their convergence. Experiments conducted on real and artificial data sets under uncertain labels and noise in the constraints show the effectiveness of using fuzzy clustering algorithm for noisy semi-supervised clustering.