Value Balanced Agglomerative Connectivity Clustering
Bibliographic record
Abstract
In this paper we propose a new clustering framework for transactional data-sets involving large numbers of customers and products. Such transactional data pose particular issues such as very high dimensionality ($>10,000$), and sparse categorical entries, that have been dealt with more effectively using a graph-based approach to clustering such as ROCK. But large transactional data raises certain other issues such as how to compare diverse products (e.g. milk vs.~cars) cluster balancing and outlier removal, that need to be addressed. We first propose a new similarity measure that takes the value of the goods purchased into account, and form a value-based graph representation based on this similarity measure. A novel value-based balancing criterion that allows the user to control the balancing of clusters, is then defined. This balancing criterion is integrated with a value-based goodness measure for merging two clusters in an agglomerative clustering routine. Since graph-based clustering algorithms are very sensitive to outliers, we also propose a fast, effective and simple outlier detection and removal method based on under-clustering or over-partitioning. The performance of the proposed clustering framework is compared with leading graph-theoretic approaches such as ROCK and METIS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".