Data Clustering with Libby-Novick Beta-Liouville Mixture Models: A Minimum Message Length Approach
Bibliographic record
Abstract
In this paper, we examine the issue of figuring out a proportional data structure without prior knowledge of the number of clusters. We model the data samples by finite mixture models based on Libby-Novick Beta-Liouville distribution, a new distribution that combines the key features of both Libby-Novick Beta and Liouville distributions. It gives high flexibility and a more robust covariance structure compared to typical distributions such as Dirichlet. However, determining the number of clusters in mixture modeling is a significant issue. In this case, the number of clusters is ascertained by applying the minimum message length (MML) approach. Furthermore, the complexity of the mixture model (that is, the number of components) can be automatically and concurrently computed with the parameters estimated in a closed form as part of the Expectation Maximization process. The proposed method is validated by both synthetic data and real-world data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".