2
$\begingroup$

I'm trying to use Kmeans clustering, with an intent to find out clusters by weighting the attributes.

Eg. if attribute A matters less than attribute B then the output should put more weight on values of attribute B and if needed give me more clusters depending on relatively small differences in B, even if this means ignoring relatively bigger differences in values of attribute A.

Any pointers/references/examples are highly appreciated.

$\endgroup$
1
1
$\begingroup$

Since the clusters are determined using the distance function, use a distance function that adds weight to required features and conversely, reduced weights on other features.

I suggest that you can start by using a weight of 1 for the required features and ignore the rest of the features. The choice of distance function itself , such as Euclidean or Manhattan, is orthogonal to the feature weighting.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.