TEMPORAL VERSUS LATEST SNAPSHOT WEB USAGE MINING USING KOHONEN SOM AND MODIFIED KOHONEN SOM BASED ON THE PROPERTIES OF ROUGH SETS THEORY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Temporal Web usage mining involves application of data mining techniques on temporal Web usage data to discover temporal usage patterns, which describe the temporal behavior of users on the Internet Web site, to understand the temporal users' behavior during different time slices. Clustering and classification are two important functions in Web mining. Classes, and associations in Web mining do not necessarily have crisp boundaries. Therefore the conventional clustering techniques became unsuitable to find such clusters and associations, where these conventional classification algorithms provide crisp classes, which are not suitable in real world applications. This gives the chance of using the non-conventional clustering techniques as fuzzy and rough sets in Web mining clustering applications. Recent research introduced the adaptation of Kohonen SOM based on the properties of rough sets theory to find the interval set clusters for the users on the Internet. This paper introduces the comparison between the latest snapshot Web usage mining and the temporal Web usage mining, and. the comparison between the temporal Web usage mining using the conventional Kohonen SOM and the modified Kohonen SOM based on the properties of sets theory.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it