Using data mining to analyze fashion consumers’ preferences from a cross-national perspective
Bibliographic record
Abstract
The purpose of this study is twofold: (1) to cluster the respondents into three consumer groups – fashion innovator, fashion follower and laggard and (2) to extract association rules from the data set in order to understand consumers’ preferences. A data-mining method was employed to analyse considerable amount of data collected from four cities as well as to understand the complexity of the diffusion process of multiple apparel products. According to the results of the present study, style was not an important factor for the fashion leaders to purchase socks in Toronto, Hangzhou and Johor Bahru. In terms of t-shirts and evening dresses/suits, 53% and 51% of fashion laggards in China had shown their strong preferences for fit and comfort, respectively. Additionally, 60% of the fashion leaders in Canada had shown a strong preference for fit and style of t-shirts. Although this study is exploratory in nature, we believe that data mining has great potential for investigating fashion diffusion of innovativeness, and more replication of this type of research will be worthwhile and meaningful.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".