Functional Decomposition of the Clustering Approach for Matrix-Based Structuring
Bibliographic record
Abstract
In engineering design, matrices have been commonly used to capture dependency relationships for structure-related problems (e.g., product architecture, process workflow, and team organization). In this context, structuring is considered a group formation process that clusters the design entities and identifies the interactions among the formed groups. To support matrix-based design structuring, this paper proposes a clustering approach that has three phases in the working procedure. Firstly, the coupling analysis is used to assess the coupling strength of any two entities according to the application context. Secondly, the sorting analysis is used to organize the matrix’s rows and columns by bringing the highly coupled entities close to each other, thus yielding a sorted matrix. Thirdly, the partitioning analysis is applied to form a structured matrix that identifies the groups of entities and their interactions based on some structural criteria (e.g., number of groups, limits on group sizes, etc). The proposed clustering method has been applied to four engineering examples to demonstrate its flexibility and adaptability in tackling different design structuring problems.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".