Sökning: "clustering"
Visar resultat 11 - 15 av 592 avhandlingar innehållade ordet clustering.
11. Essays on Distance Based (Non-Euclidean) Tests for Spatial Clustering in Inhomogeneous Populations : Adjusting for the Inhomogeneity through the Distance Used
Sammanfattning : This thesis consits of four papers dealing with distance based (non-Euclidean) tests for spatial clustering in inhomogeneous populations. The density adjusted distance (DAD), which considers the underlying density, is defined in the first paper. LÄS MER
12. Efficient Approximate Big Data Clustering: Distributed and Parallel Algorithms in the Spectrum of IoT Architectures
Sammanfattning : Clustering, the task of grouping together similar items, is a frequently used method for processing data, with numerous applications. Clustering the data generated by sensors in the Internet of Things, for instance, can be useful for monitoring and making control decisions. LÄS MER
13. Progress in Hierarchical Clustering & Minimum Weight Triangulation
Sammanfattning : In this thesis we study efficient computational methods for geometrical problems of practical importance and theoretical interest. The problems that we consider are primarily complete linkage clustering, minimum spanning trees, and approximating minimum weight triangulation. Below is a list of the main results proved in the thesis. LÄS MER
14. Functional clustering methods and marital fertility modelling
Sammanfattning : This thesis consists of two parts.The first part considers further development of a model used for marital fertility, the Coale-Trussell's fertility model, which is based on age-specific fertility rates. A new model is suggested using individual fertility data and a waiting time after pregnancies. LÄS MER
15. Resource-Aware and Personalized Federated Learning via Clustering Analysis
Sammanfattning : Today’s advancement in Artificial Intelligence (AI) enables training Machine Learning (ML) models on the daily-produced data by connected edge devices. To make the most of the data stored on the device, conventional ML approaches require gathering all individual data sets and transferring them to a central location to train a common model. LÄS MER