A Cheap Feature Selection Approach for the K-Means Algorithm.

The increase in the number of features that need to be analyzed in a wide variety of areas, such as genome sequencing, computer vision, or sensor networks, represents a challenge for the $K$ -means algorithm. In this regard, different dimensionality reduction approaches for the $K$ -means algorithm have been designed recently, leading to algorithms that have proved to generate competitive cluste...