Comparison of Discretization Approaches for Granular Association Rule Mining
Bibliographic record
Abstract
Granular association rule mining is a new relational data mining approach to reveal patterns hidden in multiple tables. Recently, granular association rules have been proposed for cold-start recommendation, where a customer or a product has just entered the system. The current research considers only nominal data. In this paper, we study the impact of discretization approaches on mining semantically richer and stronger rules from numerical data. Specifically, the equal width, the equal frequency, and the k-means approaches are adopted and compared. The setting of interval numbers is a key issue in discretization approaches. Therefore, different settings are compared through experiments on a well-known real life data set. Experimental results show that: 1) discretization is an effective preprocessing technique in mining stronger rules; 2) the appropriate settings of interval numbers are critical to obtaining more rules; 3) the equal frequency approach outperforms the equal width and the k-means approaches; and 4) the recommendation accuracy and the number of recommendations are improved significantly through the discretization approaches.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".