Introduction to the special issue on successful real-world data mining applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since its inception, the field of Data Mining and Knowledge Discovery from Databases has been driven by the need to solve practical problems [4]. From scaling to large databases and handling noisy and high-dimensional data to finding associational patterns in grocery store transaction data, data mining is a research area rich in application [1]. Despite its practical roots few case studies of data mining applications have been published. The industrial track of the annual SIGKDD conference has provided one such forum, but rarely do these papers present complete descriptions of deployed systems [2]. This special issue attempts to address the gap by showcasing the choices, strategies, and lessons learned from building a real-world data mining application. In a sense this collection is a follow-up to the first workshop on data mining case studies held during ICDM-2006 [3]. This issue however introduces several new papers. Of the 29 papers reviewed 10 papers were accepted. The papers come from a broad range of application areas including Customer Relationship Management, Medicine, Taxation, and Software Development.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it