PyramidViz: Visual Analytics and Big Data Visualization for Frequent Patterns
Bibliographic record
Abstract
Frequent pattern mining aims discover implicit, previously unknown and potentially useful knowledge in the form of frequently co-occurring items, events, or objects. These discovered frequent patterns helps reveal interesting relationships such as consumer shopper behaviour. Existing mining algorithms mostly return a long textual list of frequent patterns to users. Such a long list may not be comprehensible by many users. As a picture is worth a thousand words, visual representation of frequent patterns is more comprehensible. Consequently, several visualizers have been proposed. While they are popular and benefit from a few advantages, they also suffer from some disadvantages. In this paper, we present a visual analytic solution, called PyramidViz, for visualizing and analyzing frequent patterns. PyramidViz shows frequent patterns in an informative and intuitive fashion so that users can easily get an insight about frequency of frequent patterns and relationships (e.g., prefix- extension relationships) among related frequent patterns. Evaluation results show the effectiveness and practicality of PyramidViz in visual analytics and big data visualization of frequent patterns for various reallife applications.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".