Cluster validation indices for fMRI data: Fuzzy C-Means with feature partitions versus cluster merging strategies
Bibliographic record
Abstract
Fuzzy C-Means (FCM) is a standard technique for exploratory analysis and is readily adaptable to integrate unique data characteristics and auxiliary feature relations. Distinguishing between the spatial and temporal features of functional magnetic resonance imaging (fMRI) time courses (TC) has proved effective in reducing the presence of false positives for stimulation studies. The fuzzy partitions generated by this FCM variant (FCMP) are compared to several cluster merging techniques using cluster validation indices. These indices quantify the degree to which a dataset justifies a particular membership partition. A basic cluster merging strategies is examined where closest samples in a distance matrix are merged. A novelty is the use of alternate centroid definitions. Finally, the dynamic modeling employed by the CHAMELEON clustering algorithm is examined. All algorithms are evaluated on a Tourette's fMRI dataset.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".