问题
I have fitted a k-means algorithm on 5000+ samples using the python scikit-learn library. I want to have the 50 samples closest to a cluster center as an output. How do I perform this task?
回答1:
If km is the k-means model, the distance to the j'th centroid for each point in an array X is
d = km.transform(X)[:, j]
This gives an array of len(X) distances. The indices of the 50 closest to centroid j are
ind = np.argsort(d)[::-1][:50]
so the 50 points closest to the centroids are
X[ind]
(or use argpartition if you have a recent enough NumPy, because that's a lot faster).
回答2:
One correction to the @snarly's answer.
after performing d = km.transform(X)[:, j],
d has elements of distances to centroid(j), not similarities.
so in order to give closest top 50 indices, you should remove '-1', i.e.,
ind = np.argsort(d)[::][:50]
(normally, d has sorted score of distance in ascending order.)
Also, perhaps the shorter way of doing
ind = np.argsort(d)[::-1][:50]
could be
ind = np.argsort(d)[:-51:-1].
回答3:
If you have the distance to center values in a list, you can use sort.
results = [(distance_to_center, (x, y)), (distance_to_center, (x, y)), ...]
results.sort()
# get closest 50
closest_fifty = results[:50]
来源：https://stackoverflow.com/questions/26795535/output-50-samples-closest-to-each-cluster-center-using-scikit-learn-k-means-libr