[OC] Visualizing Distance Metrics. Data Source: Math Equations. Tools: Python. Distance metrics reveal hidden patterns: Euclidean forms circles, Manhattan makes diamonds, Chebyshev builds squares, and Minkowski blends them. Each impacts clustering, optimization, and nearest neighbor searches.

6 comments

1. Why do these all use different scales?

2. What are the different clustering uses for the methods?

3. I just had an assignment in numerical analysis where i was given different contours of shapes that had lots of noise and i needed to return the original shape it was derived from.
   i ended up using kmeans for clustering and combining that with some smoothing and traveling agent algorithms.
   what kind of clustering would you use for that case? euclidian?

4. I know some of these words.

5. Pretty interesting.

   But that color palette is a crime against data viz.

6. What do the different colors even mean? They dont seem to correspond to the same equivalence class of isocontours across the different metrics.

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