Bibliographic record
Abstract
There is an enormous amount of data generated by academic, business, and governmental organizations alike; however, only a small portion of the data that is collected and stored in databases is ever analyzed. Since data are the building blocks for both information and knowledge, the opportunity costs (to organizations) of ignoring data assets can range from competitive disadvantage to organizational demise. Data mining has thus emerged as a discipline focusing on unleashing the potential of data in organizations. The enthusiasm surrounding data mining at large continues to grow; however, at the same time, there are claims that data mining projects fail in delivering the expected value. Many of the causes of the failures can be traced back to strategy, process and technology variables. The purpose of this chapter is to discover a process for performing data mining projects and to propose this process to practitioners as a starting point when making decisions about planning, organizing, executing and closing data mining projects. Literature on package implementation, rapid application development and new product development together with results from a case study are used to arrive at the proposed data mining process. More research is needed to evaluate, refine and validate the proposed process before it can be used as the basis for developing a comprehensive methodology for performing data mining projects.
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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".