-based method for clustering random time functions which are spatially interdependent. A cluster consists of time functions which … semi-parametric model. We also assume thatthe clustering membership is a realization from a Markov random field. Under … clustering algorithm under a series of settings: small number of time points, high noise level and varying dependence structures …

transpires. Animation and activated 3D graphics of decision making are trialled as a way of providing an immersive learning …

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 …

submarkets defined by statistical clustering techniques. This finding provided useful implications for housing price prediction …