Distance-based clustering methods for large datasets

Abstract

This PhD dissertation presents a methodology focused on clustering problems with large data volumes. The goal is to design algorithms that can process large volumes of data without loss of clustering quality. Specifically, this Doctoral dissertation presents two novel, fast and scalable distance-based clustering algorithms well suited to analyse large datasets. The first one is the GPIC clustering method, which performs the calculation of the anity matrix and the eigenvectors with the support of the Graphics Processing Unit - GPU. The second method, called bdrFCM, reduces the volume of data using the border of the Fuzzy c-means cluster results as a fundamental principle. Results found with synthetic and real datasets demonstrate that the approaches proposed by this work can process a significant amount of data in less time and reduce the volume of data, whilst maintaining the quality of the clustering result