Agglomerative Clustering-Based Behavior Assessment for Customer Segmentation
Bibliographic record
Abstract
Modern business practices highly regard customer segmentation as a vital strategy, enabling companies to customize their offerings in line with the unique requirements of diverse customer segments. In an era of data-driven decision making, businesses rely on vast amounts of customer data to develop their strategies. Companies apply machine learning to gain insight into their customer base. To this end, this paper combines Exploratory Data Analysis (EDA) and Agglomerative Clustering (AC) algorithms applied to customer segmentation. The study consists of three main parts. First, data pre-processing and feature engineering are performed. Second, EDA is applied to analyze the dataset in greater depth. Third, AC is applied to capture complex customer behavior by processing hierarchical data structures. AC provides distinct benefits including adjustable detail level, the capacity to generate tree-like representations, and independence from predefined cluster quantities. The trial outcomes demonstrate successful data processing in this research and extraction of pertinent details. On the basis of this research, companies will be able to better understand their customer base and adapt their services accordingly.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".