Fuzzy Interval Number K-Means Clustering for Region Division of Pork Market
Bibliographic record
Abstract
This article proposes a new clustering algorithm named FINK-means. First, this article converts original data into a fuzzy interval number (FIN). Second, it proves the F that denotes the collection of FINs is a lattice. Finally, it introduces a novel metric distance on the lattice F. The contrast experiments about FINK-means, k-means, and FCM algorithm are carried out on two simulated datasets and four public datasets. The results show that the FINK-means algorithm has better clustering performance on three evaluation indexes including the purity, loss cost, and silhouette coefficient. FINK-means is applied to the task of region division of pork market in China based on the daily data of pork price for different provinces of China from August 9, 2017 to August 9, 2018. The results show that regions of pork market in China was divided into five categories, namely very low, low, medium, high, and very high. Every category has been discussed as well. At last, an additional experiment about region division in Canada was carried out to prove the efficiency of FINK-means further.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".