Self-Tuned Descriptive Document Clustering using a Predictive Network

Austin J. Brockmeier, Tingting Mu, Sophia Ananiadou, John Y. Goulermas

Research output: Contribution to journalArticlepeer-review


Descriptive clustering consists of automatically organizing data instances into clusters and generating a descriptive summary for each cluster. 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. Selection of descriptions often relies on heuristic criteria. We model descriptive clustering as an auto-encoder network that predicts features from cluster assignments and predicts cluster assignments from a subset of features. The subset of features used for predicting a cluster serves as its description. For text documents, the occurrence or count of words, phrases, or other attributes provides a sparse feature representation with interpretable feature labels. In the proposed network, cluster predictions are made using logistic regression models, and feature predictions rely on logistic or multinomial regression models. Optimizing these models leads to a completely self-tuned descriptive clustering approach that automatically selects the number of clusters and the number of feature for each cluster. We applied the methodology to a variety of
short text documents and showed that the selected clustering, as evidenced by the selected feature subsets, are associated with a meaningful topical organization.
Original languageEnglish
JournalIEEE Transactions on Knowledge and Data Engineering
Publication statusPublished - 1 Oct 2018


  • descriptive clustering
  • Feature selection
  • Logistic regression
  • model selection
  • sparse models


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