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)+ (Y1 -Y2)2   ) = 0

[quads id=2]

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

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

Euclidean distance (sameed, sameed) = SQRT ( (10 – 10)+ (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.

Click Here to Download 

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”.

Euclidean distance Calculations in excel after normalization - min max, z score and decimal scaling

Video Lecture

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