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The nearest neighbor method was applied to each of seven representations of the measured data.
The advantage to using nearest neighbor methods is that the institution of interest is at the center of the most similar institutions available given the variables selected for the analysis.
Among the multivariate statistical techniques, there is the Tocher's optimization and the hierarchical nearest neighbor methods (CRUZ & REGAZZI, 2004).
One of the advantages of the nearest neighbor method is that the peer groups are more refined than often-used groupings, reflecting the multidimensional diversity of health care providers.
It was shown that the searching engine based on multivariate calibration and the nearest neighbor method (MultivarSearch) gave a 2 percent better recognition rate than the tree decision-based searching engine (TreeSearch).
The nearest neighbor method (Clark and Evans 1954) was used to determine whether the C.
A third addition to the simplest nearest neighbor methods is the use of k nearest neighbors for some decisions, rather than only using a single nearest neighbor.
However, we believe that the reason for this circumstance is due to the inherent model bias and lazy characteristics of the nearest neighbor method. It lacks the ability to well describe the learning concept because the basic idea is merely distance comparisons.
Huang and Lee [22] employed a grey-based nearest neighbor method to handle the missing data problem.