Sökning: "agglomerativ klustring"

Visar resultat 1 - 5 av 9 uppsatser innehållade orden agglomerativ klustring.

1. 1. Klusteranalys : Tillämpning av agglomerativ hierarkisk och k-means klustring för att hitta bra kluster bland fotbollsspelare baserat på spelarstatistik.

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Sacko Balbas; Arvid Törnquist; [2024]
   Nyckelord :Cluster analysis; hierarchical clustering; k-means clustering; La Liga; football; algorithm; machine learning.;

   Sammanfattning : This work is about how the multivariate analysis tool cluster analysis can be appliedto find meaningfull groups of players based on player statistics. The aim of the work isan attempt to find good clusters among players within the Spanish top football divisionLa Liga for the 2022-2023 season. LÄS MER

3. 2. SARS-CoV-2 Lineage Clustering : Using Unsupervised Machine Learning

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Amanda Hedlund; Fonzie Forsman; [2022]
   Nyckelord :;

   Sammanfattning : The methods of sequencing genetic information and the access to this information has proved to be very useful in the research and understanding of viruses. It can for example be used to develop vaccines, manage pandemics, and attempt to map the virus’ spread and development. LÄS MER

4. 3. Cluster selection for Clustered Federated Learning using Min-wise Independent Permutations and Word Embeddings

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Pulasthi Raveen Bandara Harasgama; [2022]
   Nyckelord :Federated learning; Distributed machine learning; Clustering; Word Embeddings; Federerad inlärning; Distribuerad maskininlärning; Klustring; Ordinbäddningar;

   Sammanfattning : Federated learning is a widely established modern machine learning methodology where training is done directly on the client device with local client data and the local training results are shared to compute a global model. Federated learning emerged as a result of data ownership and the privacy concerns of traditional machine learning methodologies where data is collected and trained at a central location. LÄS MER

5. 4. Hierarchical Clustering in Risk-Based Portfolio Construction

   Master-uppsats, KTH/Matematisk statistik

   Författare :Natasha Nanakorn; Elin Palmgren; [2021]
   Nyckelord :Portfolio construction; asset allocation; risk-based asset allocation; hierarchical clustering; agglomerative clustering; hierarchical risk parity; risk; volatility; Portföljallokering; portföljhantering; portföljmetoder; riskbaserad portföljallokering; hierarkisk klustring; agglomerativ klustring; risk; volatilitet;

   Sammanfattning : Following the global financial crisis, both risk-based and heuristic portfolio construction methods have received much attention from both academics and practitioners since these methods do not rely on the estimation of expected returns and as such are assumed to be more stable than Markowitz's traditional mean-variance portfolio. In 2016, Lopéz de Prado presented the Hierarchical Risk Parity (HRP), a new approach to portfolio construction which combines hierarchical clustering of assets with a heuristic risk-based allocation strategy in order to increase stability and improve out-of-sample performance. LÄS MER

7. 5. Unsupervised machine learning to detect patient subgroups in electronic health records

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Elin Lütz; [2019]
   Nyckelord :Machine learning; unsupervised learning; clustering; EHR; electronic health records; ICD; diagnosis codes.; Maskininlärning; oövervakat lärande; klustring; EHR; digitala patientjournaler; ICD; diagnoskoder;

   Sammanfattning : The use of Electronic Health Records (EHR) for reporting patient data has been widely adopted by healthcare providers. This data can encompass many forms of medical information such as disease symptoms, results from laboratory tests, ICD-10 classes and other information from patients. LÄS MER