Abstract
Documents, sentences and words clustering are well studied problems. Most existing algorithms cluster documents, sentences and words separately but not simultaneously. However, when analyzing large textual corpuses, the amount of data to be processed in a single machine is usually limited by the main memory available, and the increase of these data to be analyzed leads to increasing computational workload. In this paper we present a parallel fuzzy triadic similarity measure called PFT-Sim, to calculate fuzzy memberships in a context of document co-clustering based on a parallel programming architecture. It allows computing simultaneously fuzzy co-similarity matrices between documents/sentences and sentences/words. Each one is built on the basis of the others. The PFT-SIM model provides a parallel data analysis strategy and divides the similarity computing task into parallel sub-tasks to tackle efficiency and scalability problems.