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in Machine Learning by (19k points)

It is described in Mahout in Action that normalization can slightly improve the accuracy. Can anyone explain the reason, thanks!

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Normalization is not required in some cases of machine learning, e.g. during clustering, we don't need normalization.


K-means clustering is "isotropic" in all directions of space, which means it tends to produce more or less round clusters. In this situation leaving variances unequal is equivalent to putting more weight on variables with smaller variance.

For Example:

X = [randn(100,2)+ones(100,2);...


% Introduce denormalization

% X(:, 2) = X(:, 2) * 1000 + 500;

opts = statset('Display','final');

[idx,ctrs] = kmeans(X,2,...





hold on






legend('Cluster 1','Cluster 2','Centroids',...


title('K-means with normalization')

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