Efficient Overlapping Document Clustering Using GPUs and Multi-core Systems

Overlapping clustering algorithms have been successfully applied in several contexts. Among the reported overlapping clustering algorithms, OClustR is the one showing the best trade-of between quality of the clusters and efficiency, in the task of document clustering; however, it has a quadratic computational complexity so it could be less useful in applications dealing with a very large number of documents. In this paper, we propose two parallel versions of the OClustR algorithm, specifically tailored for GPUs and multi-core CPUs, which enhance the efficiency of OClustR in problems dealing with a very large number of documents. The experimental evaluation over standard document collections showed the correctness and good performance of our proposals.