clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational ….e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of …

-means clustering were simulated within the the Farm-level Economic Model (FEM) to determine the economic impacts of four manure …

, data mining method clustering was undertaken to identify the customersegments. Two different clustering methods were tested …

model (GPPM) in which the partition process is predictor-dependent. The GPPM generalizes DP clustering to relax the …

spline basis expansion of the time-varying regression coefficients to reduce dimensionality, and downweight the influence of … dissertation, we propose a robust clustering method based on the EM algorithm applied to a modified multivariate normal density …

explain the phenomenon of geographical clustering of economic activity. Nonetheless, as many studies in these fields point out … chapter 3 the concept of LKS is defined and its importance for the theory of geographical clustering as well as for New Growth … clustering.In chapter 8 the analysis turns to the welfare implications of different types of market mediated knowledge exchanges …

This thesis is comprised of three chapters. The first investigates the implications of industrial clustering for labor … exploit establishment-level variation in agglomeration to explore the impact of clustering in the software publishing industry … on labor market outcomes. The results show that clustering makes it easier for workers to job hop among establishments …