This paper proposes a hybrid system for feature selection, allowing the degree of importance of different features to be determined. Different feature selection techniques, specifically the following four: Univariate feature ranking for classification using chi-square tests, Rank features for classification using minimum redundancy maximum relevance, Relief-based feature selection and Constrained greedy k-means with silhouette value ranking method are employed on the three well-known datasets from UCI Machine Learning Repository: Iris, Wine, and Ionosphere. Subsequently, with the variables ordered from highest to lowest importance as a result of each of the four feature selection methods, an iterative clustering process is performed, agglomerating features that are used to calculate accuracy. This accuracy is employed to determine the most relevant features. The best accuracy value for Iris dataset is 96%, for Wine dataset is 80.33%, and the best accuracy for Ionosphere is 83.19%. Experimental results obtained with the general unsupervised hybrid clustering system proposed in this paper are absolutely comparable to other studies.