Evolution and gaps in data mining research: Identifying the bibliometric landscape of data mining in management
Bibliographic record
Abstract
This study conducts a bibliometric analysis of data mining publications in the Scopus database, examining the evolution of the field from 2015 to 2024. The study examines the bibliometric structure of data mining in management. Analyzing 2,942 publications, the research identifies significant growth in data mining studies. It reveals gaps in integrating data mining with decision-making, artificial intelligence, forecasting, and sentiment analysis. Despite a large number of publications, interdisciplinary applications of data mining are limited. The scientific publication on data mining and its relationship with decision-making, artificial intelligence, forecasting, and sentiment analysis is found to be weak, showing significant research gaps in these areas. China and the USA are prominent contributors, indicating geographical concentration. The study highlights the need for broader interdisciplinary exploration in data mining beyond traditional areas, urging global researchers to diversify contributions. The analysis focuses solely on publications indexed in Scopus, potentially excluding relevant studies from other databases or sources. This study provides insights into the evolution of data mining research and identifies areas for further interdisciplinary exploration, contributing to the advancement of the field's boundaries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.034 | 0.104 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".