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How to check if customer segmentation/classification is correct?

There are several ways to use distance between a customer and a segment represented by a group of customers in this segment. One very simple way would be "train" a k-NN classifier to predict ...
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Clustering 3D with survey data

Convert your categorical choices into a range of 0 to 1. Convert your 1-10 scale to 0-1. Throw sklearn k-means at it. Use the elbow method for deciding how many clusters there are. For plotting the ...
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Online Generating Dendrograms with imported CSV file

Try Studio R biblioshiny. Its free and easy to use. Topic dendrogram are easily generated from it. Correspondence analysis is also done along with thematic maps and other visualization
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How to measure similarities between two datasets with same features?

You can use statistical approach and try computing KL-divergence between the 2 datasets (Distributions). However, the KL-Divergence output is between 0 and ∞ (0 meaning two distributions perfectly ...
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Using k-means to create labels for supervised learning

I'd suggest an alternative approach: train a regression model for each of the two networks A and B, which takes the conditions as input features and predicts the performance of the network under these ...
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I need help with which features to use for clustering

PCA is interesting but hard to interpret because it is a linear algorithm. Consequently, the result using many features will be probably not OK, overall if you have non-linear correlations or complex ...
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Cluster evolution over time

May be what you were looking for is the Rand index ? This "is a measure of the similarity between two data clusterings", in other words, if the RI is close to 1 (after repeated clustering ...
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K-Medoid Clustering with Point Weights

That technique is commonly called weighted or observation-weighted k-means. The weighting changes how the cluster centers (medoids in your case) are calculated. If you are using R, the WeightedCluster ...
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