Manhattan Distance vs Minkowski Distance

Developers should learn Manhattan Distance for applications involving grid-based algorithms, such as pathfinding in games (e meets developers should learn minkowski distance when working on machine learning tasks that involve distance-based algorithms, such as k-nearest neighbors (knn), k-means clustering, or similarity searches in high-dimensional data. Here's our take.

Manhattan Distance

Nice Pick

Developers should learn Manhattan Distance for applications involving grid-based algorithms, such as pathfinding in games (e

Pros

• g
• Related to: euclidean-distance, chebyshev-distance

Cons

• Specific tradeoffs depend on your use case

Minkowski Distance

Developers should learn Minkowski Distance when working on machine learning tasks that involve distance-based algorithms, such as k-nearest neighbors (KNN), k-means clustering, or similarity searches in high-dimensional data

Pros

• It is particularly useful in data preprocessing, feature engineering, and optimization problems where flexible distance measures are needed, allowing customization through the p parameter to suit specific data characteristics or application requirements
• Related to: euclidean-distance, manhattan-distance

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Manhattan Distance if: You want g and can live with specific tradeoffs depend on your use case.

Use Minkowski Distance if: You prioritize it is particularly useful in data preprocessing, feature engineering, and optimization problems where flexible distance measures are needed, allowing customization through the p parameter to suit specific data characteristics or application requirements over what Manhattan Distance offers.

The Bottom Line

Manhattan Distance wins

Developers should learn Manhattan Distance for applications involving grid-based algorithms, such as pathfinding in games (e