A novel algorithm is proposed for unsupervised feature selection. The algorithm efficacy is evaluated through the accuracy of several classifiers. Adequate attributes are effectively selected for several case studies. The proposal presents better results than other attribute clustering algorithms. The proposal provides similar results to supervised feature selection approaches.