Matrix-based hierarchical clustering for developing product architecture
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
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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