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Suppose you use prices and square meters, to cluster apartments. Suppose you also use a simple metric, like: distance <- sqrt(d_price*2 + d_area*2) Without
by danuker 5y ago
Suppose you use prices and square meters, to cluster apartments. Suppose you also use a simple metric, like:
distance <- sqrt(d_price*2 + d_area*2)
Without normalizing the units somehow, so that they have a meaningfully similar numeric expression, you are essentially only clustering by price, which is in the tens- or hundreds-of-thousands.
You use very little information about the area, because that is in the hundreds, at most thousands.
- jacquesm 5y agoThat is not a proper distance function.
- danuker 5y agoWhat do you mean? Is it not the Euclidean distance? https://en.wikipedia.org/wiki/Euclidean_distance https://en.wikipedia.org/wiki/Euclidean_distance
- nerdponx 5y agoThis has nothing to do with using non-Euclidean distance, though. This is a separate issue related to having features on different scales.
- danuker 5y agoIt was an example metric with coefficients of 1 for each feature. The point I was making was that the different scale of the features influences the result by a lot.