The accuracy of the K-means clustering algorithm depends upon the number of clusters that are specified at the initialization; depending upon the choice of the initial centers, the clustering results could vary significantly.
A unique fault root cause is obtained regardless of the choice of the initial variable.
The iterative solution of arising series of linear and nonlinear systems and the choice of the initial guess are addressed.
Achievable optima by these methods are directly impacted by the choice of the initial guess for the sought optimal profiles.
We find that most ternary azeotropes can be broken into pure components and binary azeotropes using only an appropriate choice of the initial feed.
Furthermore, the Boris mover is second order accurate only for a very specific choice of the initial half step needed by the algorithm to get started.
Consideration is given to the ethical, legal, regulatory, and economic aspects of clinical Xenotransplantation, and also to the choice of the initial patients for early clinical trials.
Instead, we consider a pure state variable approach and obtain the best possible choice of the initial state to apply to the second stage for each block.
We suggest possible strategies for the choice of the initial starting points that make k asymptotically smaller than O(n), thus making this method less expensive than the QZ iteration.
However, such extremal tendencies can be maximization or minimization of Shannon entropy, depending on the choice of the initial state, implying the insufficiency of a single extremal function of Shannon entropy.
For the sake of simplicity, we start from initial conditions not too far from equilibrium and show that the systems reach a stationary regime, as expected (despite no restriction is imposed in the choice of the initial conditions).
