Multi-dimensional sequential web mining by utilizing fuzzy interferencing
Bibliographic record
Abstract
There are several applications of sequential web mining, which is used to find the frequent subsequences in a web log in the World Wide Web (the web). We implemented a tool to analyze the sequential behavior of web log access patterns in multiple-dimensions. Sequences of frequent access patterns may change temporally and spatially. Based on the specified criteria like year, month, day, hours and location, the end-user is able to tune the minimum support threshold parameter intuitively using the fuzzy inference mechanism. Domain experts are can access several criteria, including minimum support threshold and number of accesses according to the user intuition, which is later, transformed into fuzzy inference parameters. We propose two different types of rule bases by considering the (support-minimum support, minimum support) and (support, minimum support), i.e., interval and case-based. To test our proposal, we used the web log dataset of the Department of Computer at the University of Calgary to analyze sequential access patterns of students during February and March carried out in the campus by taking the midterm dates into account. The results reported in this paper are promising; they demonstrate the applicability and effectiveness of the proposed approach.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".