Before revision:  One primary challenge involves clusteringthe data points into classes with interpretable differences. A challenge in clustering high dimensional data is that multimodal signatures that define clusters may only be present in a small but unknown subspace. Discovering the subspace that defines clusters provides low dimensional representations of the data which capture cluster diversity, and provides greater understanding of the system by identifying the key underlying variables.
After revision:  Discovering informative and meaningful features is crucial for clustering, classification, and low dimensional data embedding. Here we propose to construct features based on their ability to discriminate between clusters of the data which capture cluster diversity, and provides greater understanding of the system by identifying the key underlying variables.