External Validity Indices for Unsupervised Feature Selection
Bejara Javier · Frontiers in artificial intelligence and applications · 2013
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.