Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps
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
Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection. The performance of existing outlier detection methods is limited by the pattern/behaviour of the dataset; these methods may not perform well without prior knowledge of the dataset. This paper proposes a multi-level outlier detection algorithm (MCOD) that uses multi-level unsupervised learning to cluster the data and discover outliers. The proposed detection method is tested on datasets in different fields with different sizes and dimensions. Experimental analysis has shown that the proposed MCOD algorithm has the ability to improving the outlier detection rate, as compared to the traditional anomaly detection methods. Enterprises and organizations can adopt the proposed MCOD algorithm to ensure a sustainable and efficient detection of frauds/outliers to increase profitability (and/or) to enhance business outcomes.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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