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# How to do it...

We proceed as follows with the similarity measures:

1. First, calculate the similarity between two items using Euclidean distance:
```> x1 <- rnorm(30)
> x2 <- rnorm(30)
> Euclidean_dist = dist(rbind(x1,x2) ,method="euclidean")
> Euclidean_dist

x1
x2 6.427449```
1. Next, calculate the cosine similarity between two vectors. Load the lsa package:
```> library(lsa)
> vector1 = c( 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
> vector2 = c( 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0 )
> cosine(vector1,vector2)

[,1]
[1,] 0.2357023
```
1. Finally, calculate the Pearson correlation between two variables using the mtcars dataset:
`> mtcars_data <- read.csv("mtcars.csv") > rownames(mtcars_data) <- mtcars_data\$X > mtcars_data\$X <- NULL > coeff <- cor(mtcars_data, method="pearson") ...`

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