Bibliographic record
Abstract
Web usage mining applies data mining techniques to the discovery of usage patterns of web data. Web usage mining mines the secondary data which are recorded users' behavior generally kept in the web log. Web usage mining can be widely used to improve the system and site design, leading to better market decisions. A navigation pattern on the web is considered a sequence of web page accesses. A sequence is an ordered list of events, and sequential mining is used to find the correlation between events. WAP-tree (Web Access Pattern tree) mining is a sequential pattern mining technique for web log access sequences. The WAP-tree technique is based on a prefix tree, which first stores the original web access sequence database, and the frequent sequences are then mined from this tree by recursively re-constructing intermediate trees. This thesis proposes a WAP-tree based algorithm for finding frequent access sequences, which eliminates the need to reconstruct intermediate trees. In order to avoid reconstructing intermediate WAP-trees, the proposed algorithm builds the frequent header node links of the original tree in a pre-ordered fashion. It also uses position codes to identify the ancestor/descendant relationships between nodes of the tree, and finds common prefix subsequences of mined sequential patterns through a condition prefix sequence search. This results in much better response time as time for reconstructing and traversing several huge trees is saved.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .L84. Source: Masters Abstracts International, Volume: 41-04, page: 1113. Adviser: Christie Ezeife. Thesis (M.Sc.)--University of Windsor (Canada), 2002.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".