Matrix-based hierarchical clustering for developing product architecture
Bibliographic record
Abstract
Product architecture can influence different aspects of product lifecycle including manufacturing, assembly, and supply chain. The purpose of this article is to employ hierarchical cluster analysis for developing product architecture to support product variety. Design structure matrix is used to visualize and analyze product architecture in view of product modules, overlapping modules, and bus components. The proposed method for design structure matrix clustering consists of three phases. The first phase is component filtering to identify components that should be classified as bus components. The second phase is approximate structure formation that preliminarily organizes similar components to form a diagonal matrix. The third phase is partitioning analysis that finalizes the modules’ boundary to yield the structured matrix as the solution of design structure matrix clustering. To examine the solution’s quality, minimum description length from literature is used. Then, the proposed method is demonstrated via two literature examples and compared with the solutions by the manual and genetic algorithm approaches. One unique advantage of the proposed method is that the user can obtain and inspect the approximate structure in view of the diagonal matrix before finalizing the structured solution (e.g. estimate the number of modules).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".