Bibliographic record
Abstract
We propose an interactive model, RuleViz, for visualizing the entire process of knowledge discovery and data mining.The model consists of ve components according to the main ingredients of the knowledge discovery process: original data visualization, visual data reduction, visual data preprocess, visual rule discovery, and rule visualization.The RuleViz model for visualizing the process of knowledge discovery is introduced and each component is discussed.Two aspects are emphasized, human-machine interaction and process visualization.The interaction helps the KDD system navigate through the enormous search spaces and recognize the intentions of the user, and the visualization of the KDD process helps users gain better insight i n to the multidimensional data, understand the intermediate results, and interpret the discovered patterns.According to the RuleViz model, we implement a n i n teractive system, CViz, which exploits \parallel coordinates" technique to visualize the process of rule induction.The original data is visualized on the parallel coordinates, and can be interactively reduced both horizontally and vertically.Three approaches for discretizing numerical attributes are provided in the visual data preprocessing.CViz learns classi cation rules on the basis of a rule induction algorithm and presents the result as the algorithm proceeds.The discovered rules are nally visualized on the parallel coordinates with each rule being displayed as a directed \polygon", and the rule accuracy and quality are used to render the \polygons" and control the choice of rules to be displayed to avoid clutter.The CViz system has been experimented with the UCI data sets and synthesis data sets, and the results demonstrate that the RuleViz model and the implemented visualization system are useful and helpful for understanding the process of knowledge discovery and interpreting the nal results.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.317 | 0.141 |
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".