The telecommunications industry makes extensive use of data extracted from logs, alarms, traces, diagnostics, and other monitoring devices. Analyzing the generated data requires that the data be parsed, re-structured, and re-formatted. Developing custom parsers for each input format is labor-intensive and requires domain knowledge. In this paper, we describe a novel unsupervised text processing pipeline to automatically detect and label relevant data and eliminate noise using Levenshtein similarity and Agglomerative clustering. We experiment with different similarity and clustering algorithms on a selection of common data formats to verify the accuracy of the proposed technique. The results suggest that the proposed methodology has higher accuracy.