Choosing between Euclidean and Manhattan distance depends on the specific parameters of your application, including dimensionality, data characteristics, computational efficiency, geometric interpretation, and the context of use. Understanding these factors will help you select the most appropriate distance metric for your needs.

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--> When to Use Manhattan Distance 1. Grid-based layouts: Example: City streets, chessboards. 2. High-dimensional data: Example: Text document comparisons. 3. Feature differences matter: Example: Comparing categorical data or counts. --> When to Use Euclidean Distance 1. Geometric/spatial data: Example: Physical distances on a map. 2. Low-dimensional data: Example: 2D or 3D space measurements. 3. Natural Euclidean space: Example: Image processing, real-world distances.

Use Euclidean distance for continuous data and geometric measurements, and Manhattan distance for categorical, ordinal, or discrete data, as it ignores diagonal movements and focuses on step-by-step changes.

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It's better to use Minkowski Distance, because of its flexibility.

Euclidean distance and Manhattan distance are both distance metrics used in various fields, particularly in mathematics, computer science, and machine learning.

Euclidean distance and Manhattan distance are both distance metrics used in various fields, particularly in mathematics, computer science, and machine learning.

For flexibility usage, we consider the Minkowski Distance through p-values of Euclidean and Manhattan distances in a single equation.

The reason why we use Mann-Kewsky distance instead of Euclidean distance and Manhattan distance is because of its flexibility, and sometimes the nature of the data is such that we have to use both because we want to check the comparison.