Feature selection for clustering is a problem rarely addressed in the literature. Although recently there has been some work on the area, there is a lack of extensive empirical evaluation to assess the potential of each method. In this paper, we propose a new implementation of a wrapper and adapt an existing filter method to perform experiments over several data sets and compare both approaches. Results confirm the utility of feature selection for clustering and the theoretical superiority of wrapper methods. However, it raises some problems that arise from using greedy search procedures and also suggest evidence that filters are a reasonably alternative with limited computational cost.