A Visual Analytics Approach for Interactive Document Clustering
Why this work is in the frame
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Bibliographic record
Abstract
Document clustering is a necessary step in various analytical and automated activities. When guided by the user, algorithms are tailored to imprint a perspective on the clustering process that reflects the user’s understanding of the dataset. More than just allow for customized adjustment of the clusters, a visual analytics approach will provide tools for the user to draw new insights on the collection. While contributing his or her perspective, the user will also acquire a deeper understanding of the data set. To that effect, we propose a novel visual analytics system for interactive document clustering. We built our system on top of clustering algorithms that can adapt to user’s feedback. In the proposed system, initial clustering is created based on the user-defined number of clusters and the selected clustering algorithm. A set of coordinated visualizations allow the examination of the dataset and the results of the clustering. The visualization provides the user with the highlights of individual documents and understanding of the evolution of documents over the time period to which they relate. The users then interact with the process by means of changing key-terms that drive the process according to their knowledge of the documents domain. In key-term-based interaction, the user assigns a set of key-terms to each target cluster to guide the clustering algorithm. We have improved that process with a novel algorithm for choosing proper seeds for the clustering. Results demonstrate that not only the system has improved considerably its precision, but also its effectiveness in the document-based decision making. A set of quantitative experiments and a user study have been conducted to show the advantages of the approach for document analytics based on clustering. We performed and reported on the use of the framework in a real decision-making scenario that relates users discussion by email to decision making in improving patient care. Results show that the framework is useful even for more complex data sets such as email conversations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it