An Effective Method Based on Bi-Clustering and Association Rules for User Activity Analysis in Location-Based Social Network
Bibliographic record
Abstract
Examining information concerning users of Location-Based Social Networks (LBSNs) and predicting a user's activity becomes very difficult to process.However recently, biclustering algorithms have shown their effective plan to uncover sub-matrices showing unique patterns.In this paper, we present a novel scheme based on biclustering algorithms with the Apriori algorithm to extract association rules from LBSNs data The idea focuses on generating a model that explains and analyzes the user's activities to get a lot of information about his behavior.Next, we select the sub-matrices using the BCX motif algorithm.After that, the Apriori algorithm is only applied to these sub-matrices, allowing for a meaningful extraction and useful rules for classifying user profiles The proposed technique is applied to a large real dataset called Gowalla.Thus, we have extracted rules such as (X→Y [support%, Confidence% and Lift]).Furthermore, we have presented a compression study of the results by biclustering the Apriori method and the Apriori algorithm.Additionally, the extracted rules provided relevant information for analyzing users' activities.we show that biclustering with Apriori method exhibits the best performance.In summary, the model of association rule briefly described user activities and it is considered a novel tool, which can be included in the offered state-of-the-art methods.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".