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Abstract
Todeschini, R., 1989. k-Nearest neighbour method: the influence of data transformations and metrics. Chemometrics and Intelligent Laboratory Systems, 6: 213–220. The k-nearest neighbour (KNN) method is widely used in pattern recognition due to its conceptual simplicity, general applicability and efficiency. The common use of the KNN method is by using the Euclidean metric to measure distances between objects of the data set. On the other hand, the use of different metrics may greatly affect the results of pattern recognition methods but there seems to be little consensus as to which types of metrics or similarity coefficients are most generally applicable and in which cases. A study has been made of the influence of six different metrics and four data transformations on the KNN method applied to several data sets. The results show that the Lance-Williams, Manhattan and Camberra metrics give error rates comparable to and, in some cases, better than those obtained by the linear discriminant analysis. Insofar as data transformations are concerned, scaling with respect to the maximum seems to give better results.
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