Seven UAP Typologies Revealed by a Two-Step Cluster Analysis of 216 Historical Reports

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Bruehl, Little, and Powell use a two-step cluster analysis on a subsample of 216 UAP reports drawn from a 301‑report database compiled by Powell and colleagues (2023) covering 1947–2016 to identify seven patterns based on shape, size, hovering ability, electromagnetic effects, and sound, with object shape the main driver and a silhouette value of 0.6 indicating reasonable separation, while two clusters are defined mainly by absence of hovering and two by electromagnetic effects or sound, and 97% of electromagnetic observations were within 2,000 feet, showing that statistical pattern recognition can extract meaningful structure from high quality witness data and potentially refine UAP typologies beyond traditional manual classifications; published in World Futures with updates noted in 2025.

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Cluster Analysis of Features Associated with Unidentified Anomalous Phenomena Described in 216 Select Reports from 1947-2016
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Cluster Analysis of Features Associated with Unidentified Anomalous Phenomena Described in 216 Select Reports from 1947-2016

We targeted five reported UAP features described in a previous work: shape, estimated size, ability to hover, presence of electromagnetic (EM) effects, and presence of sound. The two-step clustering algorithm identified seven distinct clusters, with this model of good statistical quality (silhouette value = 0.6). Object shape was the strongest driver of clustering results although other features contributed.