Unsupervised feature selection is a dificult task because a reference partition is not available to evaluate the relevance of the features. Different consensus clustering methods have proposed to use external validity indices to assess the agreement of partitions obtained by clustering algorithms with difierent parameter values. Theses indices are independent of the characteristics of the attributes describing the data, the way the partitions are represented or the shape of the clusters. This independence allows to assess the similarity of partitions with different subsets of attributes.

The hypothesis of this paper is that the clustering of a dataset with all the attributes, even of poor quality, can be used as the basis for the exploration of the space of feature subsets. The proposal is to use external validation indices as the measure used to assess how well this information is preserved by a subset of the original attributes.