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Clusterings (text {C}_{R_1}) and (text {C}_{R_2}) are initially set by random initializations of the K-means algorithm applied to matrix (hat{{mathbf {H}}}).
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A diverse population of patients is generated by random initialization of parameters k pg, k cn, k nd, and initial conditions P0 and CA0 from uniform distribution on valid ranges (k pg ∈ [0.3, 0.6], k cn ∈ [0.03, 0.05], k nd ∈ [0.015, 0.025], P0 ∈ [0, 1], CA0 ∈ [0.0938, 0.1563]).
In order to achieve this goal, the implemented algorithm has to converge in a reliable manner without any random influences (as they may be caused by random initializations for nonlinear optimization techniques).
It also provides insight into random initializations of subspace arrangements.
Indeed, we did not succeed to reach good solutions for random initializations of the algorithm.
As a final comment, it has been observed that different random initializations of N-FINDR may produce different final solutions.
Mean and standard deviation were calculated for different random initializations of Arboretum runs.
Several alternative algorithms have been proposed in the literature to alleviate the slow convergence problems caused by a random initialization of the algorithm.
Proclus needs two parameters: number of clusters C and average dimensionality per subspace D. The subspace computation starts by a random initialization of medoids.
A trajectory of the wild-type response to an environmental perturbation was simulated by a random initialization of the simulation.
Here, for each percentage of link deletions, 100 different MSTs were extracted (by random initialization).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com