K-landmarks: Distributed dimensionality reduction for clustering quality maintenance

Due to the vast amount and pace of high-dimensional data production and their distribution among network nodes, the fields of Distributed Knowledge Discovery (DKD) and Distributed Dimensionality Reduction (DDR) have emerged as a necessity in many application areas. While a wealth of centralized dimensionality reduction (DR) algorithms is available, only few have been proposed for distributed environments, most of them adaptations of centralized ones. In this paper, we introduce K-Landmarks, a new DDR algorithm, and we evaluate its comparative performance against a set of well known distributed and centralized DR algorithms. We primarily focus on each algorithm's performance in maintaining clustering quality throughout the projection, while retaining low stress values. Our algorithm outperforms most other algorithms, showing its suitability for highly distributed environments.