Particle swarm optimization for large-scale clustering on apache spark
Bibliographic record
Abstract
We present a particle swarm optimization (PSO) clustering algorithm implemented in Apache Spark to achieve parallel big data clustering. Apache Spark is an in-memory big data analytics framework which uses parallel distributed processing to analyze large amount of data faster than most other existing data analytic tools. Spark's library of data analytic functions does not include the PSO algorithm. PSO is an evolutionary computing technique that has shown to produce more compact clusters than other partitional clustering techniques for a wide range of data. In addition PSO is a paralellizable and customizable algorithm well suited for multi-objective clustering problems. In this paper we present our implementation of a hybrid K-Means PSO (KMPSO) clustering algorithm in Apache Spark and demonstrate the performance gained in Spark by comparing our implementation with an implementation of KMPSO in MATLAB. We demonstrate that KMPSO can produce better clustering results than Spark's built-in clustering algorithms, and that Apache Spark enables efficient scaling of resources to handle large and complex workloads.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".