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As far as I know, scikit-learn has no library for ensemble clustering. On the other hand, you can apply the method on your dataset as follows: import numpy as np import ClusterEnsembles as CE kmeans1 = np.array([1, 1, 1, 2, 2, 3, 3]) kmeans2 = np.array([2, 2, 2, 3, 3, 1, 1]) kmeans3 = np.array([4, 4, 2, 2, 3, 3, 3]) kmeans4 = np.array([1, 2, np.nan, 1, 2, ...


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Since your data is sequential, you could try with sequential models (LSTM, RNN, GRU) etc. With which you can you predict what the user will select after the set of books as recommendation. In this way the input sequence length can be anything. (like, 3345, 33456, 334567 etc). But to answer your question with KMeans. I assume, all the rows are in same length. ...


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Check out the provided utility method torch_geometric.to_networkx (link to docs). I am not sure how this would handle multigraphs. There's also a from_networkx.


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Try general linear model to see how the features interact. Decide if the problem is classification or trend or anomaly then post back your discovery then I can help . I applied K-Means, TSNE dimension reduction, and PCA and saw maybe two clusters. it is not conclusive clustering. There were a few observations discovered by severity, age, and gender of the ...


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Your question implies that you want to find "latent (unobserved) variable(s)" in your data. An example of a latent variable could be a class variable. For example, social scientists may attempt to identify the political party membership (class) of a sample of the population where this variable is unknown. A traditional approach would be: Construct ...


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Why is potentially hidden in your feature explanations. Given that your Features give some indication on why, like Patient demographics data, or some other Features that you can include here, you can use them to answer the why. Like this using shapley and or eli5 you can integrate it with your Standard predictor classes and for the explanations with user ...


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No - PCA and k-means can not be used on categorical variables. Both PCA and k-means require numerical variables.


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Well, keep in mind that when you standardize/impute data you're estimating parameters. Given the conditions that you've defined and having enough data such that the estimates are good, then I don't think it should matter to use the training data or all the data (as a matter of fact, the estimate of the parameter using training data should be very similar to ...


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