Exploring Anthropometric Data through Cluster Analysis
Bibliographic record
Abstract
Anthropometric databases consisting of both multimedia and relational content are increasingly becoming commonplace. These databases are huge and contain data with diverse formats, representations and models. Data mining provides a powerful mechanism to further explore and explain the data as contained in these heterogeneous repositories, focusing on discovering new relationships which cannot be found using standard information retrieval techniques. In particular, cluster analysis is a data mining technique which is used to group data records into unlabeled classes, e.g. to group individuals with similar body types, income and education levels into a cluster, using unsupervised learning. This paper introduces cluster analysis as a method to explore 3D body scans together with the relational anthropometric and demographic data as contained in an integrated multimedia anthropometric database. The paper provides an overview of different cluster analysis algorithms and discusses the strengths and weaknesses of each approach when mining 3D objects together with relational attributes. Cluster analysis algorithms are evaluated in terms of scalability, the number of attributes that can be processed, the level of human intervention required and the characteristics of the clusters, amongst others. This is followed by a discussion on the application of cluster analysis to anthropometric data. The use of cluster analysis to group the data records into clusters based on both the 3D body scans and the relational attributes lead to a new understanding of the data and their interrelationships.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".