A Data Science and Engineering Solution for Fast K-Means Clustering of Big Data
Bibliographic record
Abstract
With advances in technology, high volumes of a wide variety of valuable data of different veracity can be easily collected or generated at a high velocity in the current era of big data. Embedded in these big data are implicit, previously unknown and potentially useful information. Hence, fast and scalable big data science and engineering solutions that mine and discover knowledge from these big data are in demand. A popular and practical data mining task is to group similar data into clusters (i.e., clustering). To cluster very large data or big data, k-means based algorithms have been widely used. Although many existing k-means algorithms give quality results, they also suffer from some problems. For instance, there are risks associated with randomly selecting the k centroids, there is a tendency to produce roughly equal circular clusters, and the runtime complexity is very high. To deal with these problems, we present in this paper a big data science and engineering solution that applies heuristic prototype-based algorithm. Evaluation results show the efficiency and scalability of this solution.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".