Grouping Interesting Patterns for Understanding Customer Behaviors
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
This article presents a highly efficient technique for pattern mining in the realm of customer behavior analysis, termed hybrid clustering patterns for customer behavior analysis (HCP-CBA). It leverages decomposition techniques to uncover relevant patterns by examining correlations among customer transactions within the dataset. Initially, the transaction dataset undergoes decomposition, grouping together transactions exhibiting high correlations. Subsequently, relevant patterns are extracted by applying a pattern mining algorithm represented by Apriori to each group. It incorporates both groups of transactions and shared items between groups. To assess the effectiveness of the HCP-CBA framework, extensive experiments are conducted across customer behavior dataset. The experimental results demonstrate notable reductions in both runtime and scalability. The full code of this research work is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YousIA/ConsumerAnalytics</uri>.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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