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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/171905
Dimensionality reduction for clustering with deep neural networks
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[eng] Nowadays, high dimensional data is ubiquitous: you can think for example in images, videos or
texts. Unfortunately, this property can harm seriously the performance of some algorithms. In
this project, I analyse how dimensionality reduction can help clustering improve its performance.
In order to do that, I distinguish three di erent clustering strategies: Traditional, two-stages and
deep clustering. In the rst one, the clustering is applied to the raw data while in the other two
it is applied to a low dimensional representation. I focus especially on the latter approach, which
has shown promising performance in the last years. The di erences between these approaches
are illustrated doing a series of experiments and visualisations and comparing the results.
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Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2019-2020 Tutors: Ferran Reverter; Esteban Vegas
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FERNÁNDEZ FELGUERA, Agustin. Dimensionality reduction for clustering with deep neural networks. [consulta: 9 de desembre de 2025]. [Disponible a: https://hdl.handle.net/2445/171905]