Design of Rule Extraction and Optimization Algorithms in Employee Performance Evaluation in Data Mining Environment
Bibliographic record
Abstract
In this study, a data-driven assessment framework integrating multi-criteria decision making, association rule mining and fuzzy clustering methods is proposed to address the scientific and objective needs of rule extraction and optimization in employee performance assessment.The TOPSIS model is improved by triangular fuzzy numbers to realize the objective ranking of business performance.The Apriori algorithm is improved to mine the association rules between competency and performance.The empirical results show that Employee 3 is ranked in the excellent grade with 101.32% task completion rate and 0.8323 relative proximity.The questionnaire results of competency quality had a significant impact on appraisal with a confidence level of 84.3%, while technical title and education were not sufficiently correlated with a confidence level of <30%.The fuzzy decision tree model generated 25 classification rules with a confidence level higher than 63.2%.And combined with the work attitude index with a weight of 0.2913 to complete the comprehensive performance assessment, the results show that the overall performance score of the employees in this enterprise is 0.81362, which is a good grade.This study makes the performance appraisal more objective, precise and efficient, and at the same time expands the application scope of data mining technology in enterprise management.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".