sklearn.cluster#
Popular unsupervised clustering algorithms.
User guide. See the Clustering and Biclustering sections for further details.
Perform Affinity Propagation Clustering of data. |
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Agglomerative Clustering. |
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Implements the BIRCH clustering algorithm. |
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Bisecting K-Means clustering. |
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Perform DBSCAN clustering from vector array or distance matrix. |
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Agglomerate features. |
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Cluster data using hierarchical density-based clustering. |
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K-Means clustering. |
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Mean shift clustering using a flat kernel. |
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Mini-Batch K-Means clustering. |
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Estimate clustering structure from vector array. |
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Spectral biclustering (Kluger, 2003). |
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Apply clustering to a projection of the normalized Laplacian. |
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Spectral Co-Clustering algorithm (Dhillon, 2001). |
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Perform Affinity Propagation Clustering of data. |
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Perform DBSCAN extraction for an arbitrary epsilon. |
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Automatically extract clusters according to the Xi-steep method. |
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Compute the OPTICS reachability graph. |
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Perform DBSCAN clustering from vector array or distance matrix. |
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Estimate the bandwidth to use with the mean-shift algorithm. |
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Perform K-means clustering algorithm. |
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Init n_clusters seeds according to k-means++. |
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Perform mean shift clustering of data using a flat kernel. |
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Apply clustering to a projection of the normalized Laplacian. |
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Ward clustering based on a Feature matrix. |