This document reports our findings on a set of text clusterig experiments, where a wide variety of models and algorithms were applied. The objective of these experiments is to investigate which are the most feasible strategies to process large amounts of information in face of the growing demands on data quality in many fields. The process of deduplication was accelerated through the division of the data set into individual subsets of similar items. In the best case scenario, each subset must contain all duplicates of each produced register, mitigating to zero the cluster s errors. It is established, although, a tolerance of 5 percent after the clustering process. The experiments show that the processing time is significantly lower, showing a 98,92 percent precision. The best accuracy/performance relation is achieved with the K-Means Algorithm using a trigram based model.