Correct Number of Clusters (CNC) Description Length in Arbitrary Shape Clustering
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Bibliographic record
Abstract
One of the main challenges in clustering unlabeled data sets is determining the unknown correct number of clusters (CNC). K-means is a well known and widely used clustering algorithm in this context which requires the correct number of the clusters for proper performance. To address this problem, various validity indices approaches aim to optimize a desired criteria based on measuring the compactness of cluster and the separation between cluster. K-MACE algorithm is a validity index clustering method based on estimating the average error between the correct cluster center and the estimated cluster center for each data point. We propose a modified version of K-MACE that is based on minimizing the CNC codelength. The proposed theory handles clusters that are arbitrary shaped and/or nonlinearly separable. Simulation results confirms superiority of the proposed method over well known validity index methods in the sense of accurate CNC estimation as well as optimizing Adjusted Random Index (ARI) and the Normalized Variation Information (NVI) measures.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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