Hi, if you have your original points stored in a numpy array, you can get all points from a cluster i by doing the following:
cluster_points = points[kmeans.labels_ == i] "kmeans.labels_" contains a list labels for each point. "kmeans.labels_ == i" creates a mask that selects only those points that belong to cluster i and the whole line then gives you the points, finally. BTW: the fit method has the raw points as input parameter, not the distance matrix. Regards, Christian prince gosavi <princegosav...@gmail.com> schrieb am Mi., 21. Feb. 2018 um 11:16 Uhr: > Hi, > I have applied Kmeans clustering using the scikit library from > > kmeans=KMeans(max_iter=4,n_clusters=10,n_init=10).fit(euclidean_dist) > > After applying the algorithm.I would like to get the data points in the > clusters so as to further use them to apply a model. > > Example: > kmeans.cluster_centers_[1] > > gives me distance array of all the data points. > > Is there any way around this available in scikit so as to get the data > points id/index. > > Regards > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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