# Euclidean distance in data mining

**What is the Euclidean distance?**

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

**Example:**

Age | Marks | |

Sameed | 10 | 90 |

Shah zeb | 6 | 95 |

**Formulae:**

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

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

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

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

Euclidean Distance is given below;

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

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