Affiliations: [a] Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico
| [b] División de Matemáticas e Ingeniería, FES Acatlán, Universidad Nacional Autónoma de México, Naucalpan, Mexico
Correspondence:
[*]
Corresponding author. Jesús Ariel Carrasco-Ochoa. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico. E-mail: [email protected].
Abstract: Document clustering has become an important task for processing the big amount of textual information available on the Internet. On the other hand, k-means is the most widely used algorithm for clustering, mainly due to its simplicity and effectiveness. However, k-means becomes slow for large and high dimensional datasets, such as document collections. Recently the FPAC algorithm was proposed to mitigate this problem, but the improvement in the speed was reached at the cost of reducing the quality of the clustering results. For this reason, in this paper, we introduce an improved FPAC algorithm, which, according our experiments on different document collections, allows obtaining better clustering results than FPAC, without highly increasing the runtime.
Keywords: Document clustering, large collection, high dimensionality
