Scaling the Growing Neural Gas for Visual Cluster Analysis
Bibliographic record
Abstract
The growing neural gas (GNG) is an unsupervised topology learning algorithm that models a data space through interconnected units that stand on the most populated areas of that space. Its output is a graph that can be visually represented on a two-dimensional plane, disclosing cluster patterns in datasets. It is common, however, for GNG to result in highly connected graphs when trained on high-dimensional data, which in turn leads to highly cluttered 2D representations that may fail to disclose meaningful patterns. Moreover, its sequential learning limits its potential for faster executions on local datasets, and, more importantly, its potential for training on distributed datasets while leveraging from the computational resources of the infrastructures in which they reside. This paper presents two methods that improve GNG for the visualization of cluster patterns in large-scale and high-dimensional datasets. The first one focuses on providing more accurate and meaningful 2D visual representations for cluster patterns of high-dimensional datasets, by avoiding connections that lead to high-dimensional graphs in the modeled topology which may, in turn, result in overplotting and clutter. The second method presented in this paper enables the use of GNG on big and distributed datasets with faster execution times, by modeling and merging separate parts of a dataset using the MapReduce model. Quantitative and qualitative evaluations show that the first method leads to the creation of lower-dimensional graph structures that provide more meaningful (and sometimes more accurate) cluster representations with less overplotting and clutter; and that the second method preserves the accuracy and meaning of the cluster representations while enabling its execution in large-scale and distributed settings.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".