[scikit-learn] Clustering with sparse matrix

Hi all, I have a sparse matrix where each row (item) has 160 features. For each of them only three or four features are different by 0. Can I do clustering with this data? I’m thinking to use PCA to reduce dimensionality. Thanks for any answer. Luigi ___

[scikit-learn] Getting the indexes of the data points after clustering using Kmeans

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_[

Re: [scikit-learn] Getting the indexes of the data points after clustering using Kmeans

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 t

Re: [scikit-learn] Getting the indexes of the data points after clustering using Kmeans

Hi, Thanks for your hint It just saved my day. Regards, Rajkumar On Wed, Feb 21, 2018 at 4:28 PM, Christian Braune < christian.braun...@gmail.com> wrote: > 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: > > clus