Bibliographic record
Abstract
In this study, we are concerned with the role of information granulation in processes of data mining in databases. By its nature, data mining pursuits are very much oriented towards end-users and imply that any results need to be easily interpretable. Granulation of information promotes this interpretability and channels all pursuits of data mining (that are otherwise computationally intensive and thus highly prohibitive) towards more efficient processing and feasible processing of information granules. First, we discuss the essence of information granulation and afterwards elaborate on the main approaches to the design of information granules. We distinguish between user-driven, data-driven and hybrid methods of information granulation. Several main classes of membership function of information granules — fuzzy sets are investigated and contrasted in terms of some selection criteria such as parametric flexibility and sensitivity of the ensuing information granules. We revisit two fundamental concep ts in data mining such as associations and rules in the setting of information granules. Associations are direction-free constructs that capture the most essential components of the overall structure in database. The relevance of associations is expressed by counting the amount of data standing behind the Cartesian products of the information granules contributing to the construction of the associations. The proposed methodology of data mining comprises two phases. First, associations are constructed and the most essential (relevant) ones are collected in the form of a data mining agenda. Second, some of them are converted into direction-driven constructs, that is rules. The idea of consistency of the rules is discussed in detail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".