A Parallel Comparator of Documents

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 PFTSim, 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.

Fuzzy Learning of Co-Similarities from Large-Scale Documents

“…To combine multiple occurrences of FT-Sim, we can adopt two scenarios: the first represented by a cascade topology of a co-similarity computing of multi-view data, and the second by an aggregation topology (Sassi-Hidriet al, 2014;Alouane-Ksouri et al, 2013b).…”

Efficient Pairwise Document Similarity Computation in Big Datasets

“…For large document collections and query sets, this can quickly become impractical. The importance of document similarity is recognized in many publications especially nowadays where access to information is nearly unrestricted and a culture for sharing without attribution is a recognized problem [5,14].…”