Web Mining from Interpretable Compressed Representation of Sparse Web
Bibliographic record
Abstract
Large datasets often contain computational constraints when under the non-trivial extraction of implicit, previously unknown, and potentially useful information. These datasets are everywhere, with a popular example being the World Wide Web. It acts as a mass data producer and consumer across multiple devices in a distributed fashion worldwide, containing massive amounts of data. The discovery of knowledge on the Web requires web intelligence solutions, which take advantages of data mining and data science. In the case of web mining, the mining of web structures provides commonly recommended web pages to web surfers by examining incoming and outgoing links on web pages. The overall size of the web is however sparse. Sparsity of the web comes from a high number of vertex nodes (i.e., web pages), with a small number of directed edges (i.e., incoming and outgoing hyperlinks between web pages). In this paper, we present a solution to the mining of frequent patterns from the sparse web. From the sparsity of the web, web pages are captured in compressed bitmaps that are then mined for discovery of these patterns. Our bitmap model ensures readability, flexibility, and allows for the capturing of important information across multiple 31-bit groups. The mining process is demonstrated on real-life web data to present its capacity of mining for interesting patterns from interpretable compressed representation of sparse data.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".