Skip to content
GFunnel
Create A Workspace

The GFunnel Methodology

Pattern Recognition Algorithm

All algorithms

The steps

  1. Capture the data without filtering. Per Variable Principle: hold every observation open. Filtering during capture destroys pattern visibility.
  2. Sort the data along multiple axes. Time, location, actor, outcome, context. Patterns invisible on one axis become visible on another.
  3. Per Correspondence: check whether the pattern at this scale appears at adjacent scales. Cross-scale recurrence is high-confidence pattern; single-scale appearance may be coincidence.
  4. Per the Variable Principle: name the variables the pattern depends on. A “pattern” that requires arbitrary variable selection is overfitting.
  5. Test the pattern’s falsifiability. What observation would prove the pattern wrong? If nothing, it is not a pattern — it is a story.
  6. Per Layer I.G: prior failed pattern hypotheses are integrated into your current pattern recognition capacity. Your detection is constituted by every prior misidentification.
  7. Document the pattern. Per Database step. Patterns articulated externally become testable; patterns held only internally drift.

Articles on this

No articles on this one yet.

From the GFunnel Methodology v5.1 by Cameron Garlick, licensed CC BY 4.0. Steps are reproduced word for word.