Bibliographic record
Abstract
Frequent itemset mining discovers implicit, previously unknown and potentially useful knowledge---in the form of frequent itemsets---from data. For example, discovery of frequently purchased merchandise products reveals customer purchase patterns, which help store managers about their business strategies and promotional tactics. These, in turn, help increase profits of the stores. As another example, discovery of popular collections of courses reveals course popularity and trends of some subject matters. These, in turn, assist university administrators schedule courses and their corresponding exams to avoid conflict or exam hardship, as well as planning of the calendar. As we are living in the era of big data, many applications and services generate high volumes of a wide variety of highly valuable data at a high velocity. These data can be of a wide range of veracity. Consequently, having scalable frequent itemset mining service is important to both the data mining experts and non-experts. Over the past two decades, numerous frequent itemset mining algorithms have been proposed. Many of them require some degrees of data mining knowledge and expertise, which may be inaccessible by layman. In this paper, we propose a tool with an intention to provide scalable frequent itemset mining-as-a-service (FIMaaS) on cloud for non-expert data miners.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.228 |
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".