A Visualization Model of Interactive Knowledge Discovery Systems and Its Implementations
Bibliographic record
Abstract
We briefly introduce an interactive visualization model, RuleViz, for knowledge discovery and data mining, which consists of five components: data preparation and visualization, interactive data reduction, data preprocessing, pattern discovery, and pattern visualization. With this model, the implementation issues are considered and three implementation paradigms, including image-based paradigm, algorithm-embedded paradigm, and interaction-driven paradigm, are discussed. We implement an interactive visualization system, AViz, which discovers 3D numerical association rules from large data sets based on the image-based paradigm. The framework of the AViz system is presented and each component is explored. To discretize numerical attributes, three approaches, including equal-sized, bin-packing-based equal-depth, and interaction-based approaches are proposed, and the algorithm for mining and visualizing numerical association rules is developed. Our experimental result on a census data set is illustrated, which shows that the AViz system is useful and helpful for discovering and visualizing numerical association rules.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".