Recently, the number of features in different problem domains has grown enormously. In order to select the best representation (attributes) for these problems, a deep knowledge of the problem domain is required. As this type of knowledge is not always possible, feature selection needs to be applied as an automatic selection process of the most relevant attributes in a dataset. In this paper, we propose a new dynamic feature selection technique using data clustering algorithms to select features in a dynamic way and the selected features will be used in classification methods. Our technique aims to select the best attributes for a group of instances rather than to the entire dataset, leading to a dynamic way to select attributes. We will also carry out an empirical analysis using well-known existing methods. Our findings indicated gains when comparing the proposed methods to the existing ones, for the majority of cases.