![]() ![]() In the proposed network, cluster predictions are made using logistic regression models, and feature predictions rely on logistic or multinomial regression models. For text documents, the occurrence or count of words, phrases, or other attributes provides a sparse feature representation with interpretable feature labels. The subset of features used for predicting a cluster serves as its description. We model descriptive clustering as an auto-encoder network that predicts features from cluster assignments and predicts cluster assignments from a subset of features. Selection of descriptions often relies on heuristic criteria. ![]() ![]() The description should inform a user about the contents of each cluster without further examination of the specific instances, enabling a user to rapidly scan for relevant clusters. Descriptive clustering consists of automatically organizing data instances into clusters and generating a descriptive summary for each cluster. ![]()
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