classification experiments with the k-Nearest Neighbor algorithm.

Hi everyone

It's my first effort for KNN classification and am using wine data sample from UCI website. i was wondering what distance measures I can use in this case and how to get the computation time required to compute the distance from a sample to each other sample.

I want to determine its k nearest neighbors for each labeled sample, if the label of the sample is more common than any other label for the k nearest neighbors, then count the classification as correct. Otherwise, count the classification as incorrect.

I'd appreciate any input

Thanks

data set info: attributes :
      1) Alcohol
	2) Malic acid
	3) Ash
	4) Alcalinity of ash  
and ... 13)xxx

it categorizes wines into 3 classes

one example would be:

1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065

Measuring computation time is difficult to do right, especially if what you are trying to do is predict how efficient different methods would be with larger datasets.

Thanks walter, this dataset is small, only 178 lines each having 13 attributes

Answer 1

For timing, see the File Exchange Contribution timeit

Thanks but how exactly should I use this function in KNN code?