I have a data set (after normalising and preprocessing) contains a data frame that has 5 columns and 133763 rows.

I am trying to apply k means algorithm, and herical algorithm in order to do the clustering. However, my problem is that **R studio keeps trying to do the calculation, and then it throws out of memory exception** even though i am using **mac bro i 7, 16 gb**

my code for heroical clustering is:

```
dist.cards<-dist(cardsNorm)
```

as i said that takes forever running. however, if i did this

```
dist.cards<-dist(cardsNorm[1:10])
```

it works fine, that is because i just use 10 rows.

for the k mean, this is my code:

```
cardsKMS<-kmeans(cardsNorm,centers=3,iter.max = 100,nstart = 25)
```

it works fine, but when i try to measure the model using this code

```
a <- silhouette(cardsKMS$cluster,dist(cardsNorm))
```

it takes forever and never finishes calculating

help please

Answer:

Creating a distance matrix between `n = 133763`

observations requires `(n^2-n)/2`

pairwise comparisons. Given that a scalar numeric requires 12 bytes of RAM the entire matrix requires about 100 GB. So unfortunately you don't have enough.

Algorithms based on distance matrices scale very poorly with increased data set size (since they are inherently quadratic in memory and CPU) so I am afraid you need to try some other clustering algorithm.

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