Large-Scale Mining of Co-occurrences: Challenges and Solutions
Bibliographic record
Abstract
The ability to extract frequent pairs from a set of baskets (or frequent word co-occurrences from a set of documents) is one of the fundamental building blocks of data mining. When the number of items in a given basket is relatively small the problem is trivial. Even when dealing with millions of baskets it is still trivial providing that the number of unique items in the basket set is small. The problem becomes much more challenging when we deal with millions of baskets, each containing hundreds of items that are part of a set of millions of potential items. Especially when we are looking for highly correlated results at extremely low support levels. A particularly difficult case is when "items" are words and "baskets" are long documents in a very large text corpus. For 17 years the Direct Hashing and Pruning Park Chen Yu (PCY) Algorithm has been the principal technique used when there are billions of potential pairs that need to be counted. In this paper we show new approaches that allow us to take full advantage of both multi-core and multi-CPU setups for cases where PCY fails and Map-Reduce struggles, offering excellent performance scaling when the number of processors, unique items and items per transaction are at their highest. We believe that our approaches have much broader applicability in the field of co-occurrence counting, and can be used to generate much more interesting results when mining very large data sets.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".