Introduction This project applies text clustering techniques to a collection of documents written by two different authors with distinct interests. The aim is to automatically group the texts into meaningful categories or topics without prior knowledge of their authorship. Using natural language processing and unsupervised learning methods in R—specifically TF-IDF vectorization and k-means clustering—we analyze word usage patterns to uncover underlying themes and stylistic differences between the authors.

Integrantes: Maria Lucía Castillo García, Juliana González Sánchez y Ana Daniela Paredes Tovar.

Integrantes: Maria Lucía Castillo García, Juliana González Sánchez y Ana Daniela Paredes Tovar.