Parallelizing Active Memory Ants with MapReduce for Clustering Financial Time Series Data
Bibliographic record
Abstract
Clustering financial time series data is a computationally intensive problem. Ant brood clustering (ABC) is a meta-heuristic algorithm inspired by how ants sort the broods in their nest according to their shape and size. In this paper, ants are incorporated with short term memory to avoid redundant random walks, a drawback experienced in original ABC. This algorithm, called ABC-INTE (Ant Brood Clustering with Intelligent Ants), is applied to cluster financial time series data using MapReduce programming model. Our algorithm employs alternate number of mappers over multiple MapReduce iterations to exploit data parallelism and indirect communication among mappers. We evaluate the clustering quality, by using the sum of squares for the mean intra-cluster distance (MICD) to measure the intra-cluster distance and sum of squares of the inter-clusters distance (SSICD) to measure the inter-cluster distance. Our algorithm achieves a value of 475.47 for the ratio SSICD/SS(MICD), which outperforms both sequential implementation and the MapReduce implementation with multiple mappers but no multiple MapReduce iterations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".