I did k-means clustering with 2d data. The x axis represent depth (km). And result was :.
But when I converted km to metr, I got it
Could you give explanation why do this happens?
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1 Answer
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When you change the scale or range of your features the euclidean distance between pairs of observations in your dataset changes significantly and one feature with the larger range dominates over all the others.
So, in your case, k-means was tricked into believing that the depth in metres is more important than the other feature with the lower scale.
You can fix this by normalizing all features in your dataset to a similar scale so as to reduce the bias towards specific features.
