# How to compute CLARA (Clustering Large Applications) in R

CLARA, short for Clustering Large Applications, is a statistical method that is used to perform cluster analysis on large datasets. It is an efficient way to perform cluster analysis by taking random samples from the large dataset and then applying a traditional clustering method, such as k-means, to each sample. The results from each sample are then combined to form a final clustering solution.

The idea behind CLARA is that by working with smaller samples of the large dataset, the computational time and memory requirements are greatly reduced. However, by using multiple samples, the final solution is more robust and likely to be more accurate than using a single sample.

In R, CLARA can be computed using the “clara()” function from the “cluster” package. The function takes several arguments, such as the dataset, the number of random samples to be taken, the number of clusters, and the clustering method to be used. Once the function is run, it returns the final clustering solution, which can then be visualized and interpreted.

It is worth noting that CLARA is not the only way to handle large datasets, other methods are also available such as Mini-batch k-means, and Streaming k-means. These methods are similar to CLARA in that they involve working with smaller subsets of the data, but they have their own specific advantages and disadvantages. It’s important to consider the nature of the dataset and the computational resources available to you when choosing which method to use.

In summary, CLARA is a statistical method that is used to perform cluster analysis on large datasets. It is an efficient way to perform cluster analysis by taking random samples from the large dataset and then applying a traditional clustering method, such as k-means, to each sample. The results from each sample are then combined to form a final clustering solution. In R, CLARA can be computed using the “clara()” function from the “cluster” package. Other methods such as Mini-batch k-means and Streaming k-means also exists for handling large datasets, so it’s important to consider the nature of the dataset and the computational resources available to you when choosing which method to use.

In this Applied Machine Learning Recipe, you will learn: How to compute CLARA (Clustering Large Applications) in R.

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