.. _auto_fastener_cluster:

*FASTENER CLUSTER (K-MEANS)*
============================

Groups fasteners into k clusters using k-means with k-means++ initialization on selected numeric features (default: peak_axial_kN, peak_shear_kN, fos_min). Returns per-fastener cluster labels + per-cluster summary (count, centroid in real units, sample IDs). Useful for identifying 'families' of similarly-loaded joints in large datasets (e.g. cluster the BiW bolts that all see similar crash load profiles, separate from the few outliers driving failures). Standalone in-PHP implementation — no external ML dependencies. Pipe master_table from lsdyna_fastener_failure_analysis or lsdyna_joint_map_batch_analysis directly into this worker.

When to use
-----------

Tagged: ``cluster``, ``fastener``, ``fos``, ``grouping``, ``k-means``, ``kmeans``, ``ml``, ``outlier``.

Inputs
------

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   :header-rows: 1
   :widths: 20 20 20 20 20 20

   * - Label
     - ID
     - Type
     - Default
     - Required
     - Description
   * - Master Table
     - master_table
     - dataset
     - —
     - 
     - Per-fastener row dataset from a previous fastener-analysis worker.
   * - Number of Clusters (k)
     - num_clusters
     - integer
     - 4
     - 
     - Target cluster count (2-20 typical).
   * - Features (CSV)
     - features
     - string
     - peak_axial_kN,peak_shear_kN,fos_min
     - 
     - Comma-separated numeric column names from master_table to cluster on.
   * - Max Iterations
     - max_iterations
     - integer
     - 50
     - 
     - k-means iteration cap (converges early on most datasets).
   * - Random Seed
     - random_seed
     - integer
     - 42
     - 
     - Seed for reproducible k-means++ initialization.

Outputs
-------

.. list-table::
   :header-rows: 1
   :widths: 20 20 20 20

   * - Label
     - ID
     - Type
     - Description
   * - Cluster Labels
     - labels
     - dataset
     - Per-fastener: id + cluster integer (0 .. k-1).
   * - Cluster Summary
     - cluster_summary
     - dataset
     - Per-cluster: count, real-unit centroid for each feature, z-score centroid, up to 10 sample IDs.
   * - Meta
     - meta
     - dataset
     - Run metadata — k, features used, iterations, points used/skipped, feature mean/std stats.

Disciplines
-----------

- engineering.fastener.analysis
- ml.unsupervised.clustering

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   Auto-generated from <code>platform</code> schema. Worker id: <code>fastener_cluster</code>. Schema hash: <code>39417225968e</code>. Hand-curated docs in <code>workerexamples/</code> override this page when present.
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