ICE builds an interpretable decision tree for clustering ensemble by splitting on original features to maximize summed chi-squared agreement with base k-means partitions.
Agresti,Categorical data analysis
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Interpretable Clustering Ensemble
ICE builds an interpretable decision tree for clustering ensemble by splitting on original features to maximize summed chi-squared agreement with base k-means partitions.