How to compute CLARA (Clustering Large Applications) in R

Hits: 337

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.



How to compute CLARA (Clustering Large Applications) in R

Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!

https://setscholars.net/data-analytics-types-of-clustering-methods-overview-and-quick-start-r-code/

Machine Learning for Beginners in Python: k-Means Clustering

Machine Learning for Beginners in Python: How to Group Observations Using K-Means Clustering