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 …

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

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 …

small number of replications per variable, and HDLLSS data refer to HDLSS data observed over time. Clustering technique …, mass spectrometry data, pattern recognition. Most current clustering algorithms for HDLSS and HDLLSS data are adaptations … addition, available algorithms often exhibit poor clustering accuracy and stability for non-normal data. Simulations show that …

of the pattern relationship is introduced to pattern clustering. By searching for the Gaussian-gravity-guided nearest … neighbour of each pattern, this clustering method can easily determine the boundaries of the clusters. Third, a method that … share prices by time series clustering, principal component analysis, independent component analysis, and object recognition …