Hong Kong Baptist University
Cluster analysis plays a critical role in a wide variety of applications; but it is now facing the computational challenge due to the continuously increasing data volume. Parallel computing is one of the most promising solutions to overcoming the computational challenge. In this paper, the authors target at parallelizing k-means, which is one of the most popular clustering algorithms, by using the widely available Graphics Processing Units (GPUs). Different from existing GPU-based k-means algorithms, they observe that data dimensionality is an important factor that should be taken into consideration when parallelizing k-means on GPUs.