t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
https://doi.org/10.1109/TVCG.2020.2986996
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14 lines
598 B
14 lines
598 B
# first line: 239
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def Clustering(similarity):
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similarityNP = np.array(similarity)
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n_clusters = 25 # change that to send less diverse projections
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kmedoids = KMedoids(n_clusters=n_clusters, random_state=0, metric='precomputed').fit(similarityNP)
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global dataProc
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clusterIndex = []
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for c in range(n_clusters):
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cluster_indices = np.argwhere(kmedoids.labels_ == c).reshape(-1,)
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D_c = similarityNP[cluster_indices][:, cluster_indices]
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center = np.argmin(np.sum(D_c, axis=0))
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clusterIndex.append(cluster_indices[center])
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return clusterIndex
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