**What is the Euclidean distance?**

Euclidean distance is a technique used to find the distance/dissimilarity among objects.

**Example:**

Â | Age |
Marks |

Sameed | 10 | 90 |

Shah zeb | 6 | 95 |

**Formulae:**

Euclidean distance (sameed, sameed) = **SQRT (** Â (X1 – X2)^{2Â }+ (Y1 -Y2)^{2 Â Â }**)** =Â 0

[quads id=2]

Euclidean distance (sameed, sameed) = SQRT ( (10 – 10)^{2Â }+ (90 -90)^{2}) =Â 0

**Here note** that (90-95) = -5 and when we take sqaure of a negative number then it will be a positive number. For example, (-5)^{2} = 25

Euclidean distance (sameed, shah zeb) = SQRT ( (10 – 6)^{2Â }+ (90 -95)^{2}) =Â 6.40312

Euclidean distance (shah zeb, sameed) = SQRT ( (10 – 6)^{2Â }+ (90 -95)^{2}) =Â 6.40312

Euclidean distance (sameed, sameed) = SQRT ( (10 – 10)^{2Â }+ (90 -90)^{2}) =Â 0

Euclidean Distance is given below;

Â | Sameed |
Shah zeb |

Sameed |
0 | 6.40312 |

Shah zeb |
6.40312 | 0 |

## Download Excel File Calculations

This file contains the Euclidean distance of the data after the min-max, decimal scaling, and Z-Score normalization.

## Euclidean distance after the min-max, decimal scaling, and Z-Score normalization

Let’s see the “Euclidean distance after the min-max, decimal scaling, and Z-Score normalization”.

## Video Lecture

## Next Similar Tutorials

- Proximity Measure for Nominal Attributes – Click Here
- Distance measure for asymmetric binary attributes – Click Here
- Distance measure for symmetric binary variables – Click Here
- Euclidean distance in data mining – Click Here Euclidean distance Excel file – Click Here
- Jaccard coefficient similarity measure for asymmetric binary variables – Click Here