BitBooster: Effective Approximation of Distance Metrics via Binary Operations

The Euclidean distance is one of the most commonly used distance metrics. Several approximations have been pro-posed in the literature to reduce the complexity of this metric for high-dimensional or large datasets. In this paper, we propose BitBooster, an approximation to the Euclidean distance that can be efficiently computed using binary operations and which can also be applied to the Manhattan distance. The introduced approximation error is shown to be negligible when BitBooster is used for both convex- and density-based clustering. While obtaining clusters of almost the same quality as those obtained with the exact computation, we require only a fraction of the computation time. We demonstrate the superiority of our method to alternative approximations on 960 synthetic and 13 real-world datasets of varying sizes, dimensions and clusters.