Bibliographic record
Abstract
Many applications such as intelligent tutoring system (ITS) use data that are better represented as binary data. This paper presents a novel algorithm called MBER (Mining Binary Data Efficiently by Reduced AND operations) for finding frequent itemsets in a binary dataset using matrix algebra operations. Frequent itemsets are sets of items in a transactional database that occur together frequently (defined by a user-given threshold value called minimum support). Existing algorithms that operate on binary data, such as ABBM, generate frequent itemsets by performing exhaustive AND operations using brute force method. MBER, on the other hand, generates frequent itemsets using a novel technique in which it first uses matrix algebra operations to find those transactions that have m common items in them (called as potential transactions) and then performs AND operations on only such potential transactions. This reduces the total number of AND operations required considerably (by less than a quarter) and thereby improves the efficiency of the algorithm. MBER also shows a significant improvement over traditional algorithms that generate frequent itemsets, such as Apriori, by eliminating the need to (i) scan the database more than once and (ii) to generate large number of candidate itemsets. This paper concludes by a proof of correctness of MBER and a discussion on evaluating it.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".