Research on Optimization Methods of Knowledge Discovery Algorithms for Library Data Mining
Bibliographic record
Abstract
This study focuses on library data mining scenarios and proposes an optimization method for the deficiencies of existing knowledge discovery algorithms in terms of efficiency, accuracy and interpretability.The method first uses principal component analysis to downscale library highdimensional data to extract the main features and improve the data mining efficiency.Then, the fuzzy clustering algorithm is used to cluster the dimensionality reduced data to more accurately identify the user groups, resource categories and other implicit knowledge.The clustering results are interpreted and analyzed to provide data support for knowledge discovery in library data mining.The algorithm in this paper demonstrates better performance in data dimensionality reduction at the level of memory usage as well as time consumption, and identifies three major components with cumulative contribution of more than 80%.In addition, the algorithm achieves an average purity of 95.45% for book data clustering and a clustering time consumption of 3.47s with a data stream of 300unit k, both of which are better than the comparison algorithms.The comprehensiveness weight of a university's book resources is 0.17, which is the highest performance, while the practicality and standardization are the next highest, 0.155 and 0.152, respectively.It can be seen from the clustering that the book category with the highest borrowing rate is science and technology, and the lowest one is literature, which reflects the user's demand for knowledge of a specific field.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".