Decision-grade dashboard for selecting the optimal number of clusters (K). The recommendation is based exclusively on deterministic structural signal extracted from the data. Standard clustering metrics are shown for comparison only.
K = 5 is selected as the optimal segmentation level based on deterministic structural evidence.
This level maximizes the structural signal gain extracted from the data structure, with a 4.30× gain compared to baseline segmentation (K=2).
The exploration is bounded at K = 16.
Structural signal gain across K. The recommended K is highlighted by a vertical marker.
Reproducibility, traceability and execution metadata.
Structural metrics drive the decision. Other metrics provide contextual information.
The recommended K is visually emphasized. Structural metrics are the decision layer; standard metrics remain informative.
| K | Recommended | Acceptable | Structural Signal Gain | Inertia Ratio | Calinski–Harabasz | Davies–Bouldin | Multi-Segment Structure |
|---|---|---|---|---|---|---|---|
| 2 | No | No | 1.0000 | 0.7457 | 3410313.577 | 1.6636 | 2 |
| 3 | No | No | 1.6030 | 0.5248 | 4526844.200 | 1.0140 | 3 |
| 4 | No | No | 2.3621 | 0.3239 | 6957131.506 | 0.8887 | 4 |
| 5 | Yes | Yes | 4.3036 | 0.1601 | 13117515.379 | 0.5768 | 5 |
| 6 | No | Yes | 2.9016 | 0.1568 | 10754488.372 | 1.3568 | 6 |
| 7 | No | Yes | 2.8810 | 0.1535 | 9188098.531 | 1.9211 | 9 |
| 8 | No | No | 2.0944 | 0.1503 | 8077632.451 | 2.3323 | 14 |
| 9 | No | No | 1.6554 | 0.1470 | 7252388.473 | 2.6528 | 22 |
| 10 | No | No | 1.9493 | 0.1438 | 6617871.482 | 2.9138 | 35 |
| 11 | No | No | 0.5615 | 0.1419 | 6048583.284 | 2.8438 | 55 |
| 12 | No | No | 0.5540 | 0.1400 | 5584882.325 | 2.7859 | 77 |
| 13 | No | No | 0.5531 | 0.1381 | 5200705.935 | 2.7367 | 96 |
| 14 | No | No | 0.5604 | 0.1362 | 4877589.553 | 2.6947 | 124 |
| 15 | No | No | 0.5746 | 0.1343 | 4602629.865 | 2.6581 | 157 |
| 16 | No | No | 0.5656 | 0.1333 | 4335061.331 | 2.6865 | 234 |
Displayed for compatibility with standard clustering practices. These metrics typically do not identify a clear optimal K and are not used in the decision mechanism.
Shows the richness of segmentation across K, enabling multi-dimensional analysis beyond single-label clustering.
This analysis is based on deterministic algorithms designed for reproducibility, transparency, and auditability. The results reflect structural patterns in the data and do not imply causality or prediction. Human expertise and domain knowledge remain essential for interpretation, validation, and decision-making. No personal data is retained beyond derived analytical artifacts.
This analysis is based solely on the input dataset and its structural properties. Results may vary depending on data quality, feature selection, preprocessing choices, and business context. The recommended segmentation should be interpreted alongside domain-specific knowledge and operational constraints.
The recommended number of clusters (K) is derived exclusively from deterministic structural signal. Standard clustering metrics are displayed for compatibility with industry practices, but they are not used in the decision mechanism.
This dashboard provides an executive-level decision output. A full expert-level analysis is also available, covering the complete evaluation of segmentation across K, including structural metrics, topology insights, and detailed cluster-level statistics.
The analysis also provides explainability through surrogate models, full auditability of the decision process, and optional predictive capabilities for operational deployment. This expert analysis bridges deterministic data structure understanding with real-world decision-making.
The selected model is delivered in a production-ready format, enabling immediate integration into downstream systems and workflows.
This includes a structured representation of the model as well as detailed scoring outputs, allowing consistent assignment of new data points and flexible multi-dimensional evaluation of populations.
These outputs support both automated deployment and advanced analytical use cases, ensuring continuity from decision-making to operational execution. Segmentation decisions can therefore be reliably operationalized across business processes and systems.
All results are reproducible based on deterministic computation. The analysis can be audited through the execution metadata, engine version, and dataset fingerprint. No stochastic processes are involved in the decision mechanism.