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The k-NN algorithm can also be adapted for use in estimating continuous variables. One such implementation uses an inverse distance weighted average of the k-nearest multivariate neighbors. This algorithm functions as follows: Compute Euclidean or Mahalanobis distance from target plot to those that were sampled. Order samples taking for account calculated distances. Choose heuristically optimal k nearest neighbor based on RMSE done by cross validation technique. Calculate an inverse distance weighted average with the k-nearest multivariate neighbors. Using a weighted k-NN also significantly improves the results: the class (or value, in regression problems) of each of the k nearest points is multiplied by a weight proportional to the inverse of the distance between that point and the point for which the class is to be predicted. http://en.wikipedia....ghbor_algorithm if you looking for idw tools in arcgis, it's under interpolation toolset of spatial analyst tools

k nearest is inside the idw, its the number of neighbors getting used in interpolation, instead of krigging regression use the krigging iterpolation

Natural neighbor interpolation is a method of spatial interpolation, developed by Robin Sibson. The method is based on Voronoi tessellation of a discrete set of spatial points. This has advantages over simpler methods of interpolation, such as nearest neighbor, in that it provides a more smooth approximation to the underlying "true" function. http://en.wikipedia.org/wiki/Natural_neighbor it's under interpolation toolset of spatial analyst tools