A Filter Feature Selection Method for Clustering

High dimensionnal data is a challenge for the KDD community. Feature Selection (FS) is an efficient preprocessing step for dimensionnality reduction thanks to the removal of redundant and/or noisy features. Few and mostly recent FS methods have been proposed for clustering. Furthermore, most of them are ”wrapper” methods that require the use of clustering algorithms for evaluating the selected features subsets. Due to this reliance on clustering algorithms that often require parameters settings (such as number of clusters), and due to the lack of a consensual suitable criterion to evaluate clustering quality in different subspaces, the wrapper approach can not be considered as a universal way to perform FS within the clustering framework. Thus, we propose and evaluate in this paper a ”filter” FS method. This approach is consequently completely independent of any clustering algorithm. It is based upon the use of two specific indices that allow to assess the adequacy between two sets of features. As these indices exhibit very specific and interesting properties as far as their computational cost is concerned (they just require one dataset scan), the proposed method can be considered as an effective method not only from the point of view of the results quality but also from the executing time point of view...