Most popular machine learning R packages – part 2

In a previous post: Most popular machine learning R packages, trying to hash out what are the most frequently used machine learning packages, I simply chose few names from my own memory. However, there is a CRAN task views web page which “aims to provide some guidance which packages on CRAN are relevant for tasks related to a certain topic.” So instead of relying on my own experience, in this post I correct for the bias by simply looking at the topic
Machine Learning & Statistical Learning. There are currently around 100 of those packages on CRAN.

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Understanding K-Means Clustering

Introduction

Google “K-means clustering”, and you usually you find ugly explanations and math-heavy sensational formulas*. It is my opinion that you can only understand those explanations if you don’t need them; meaning you are already familiar with the topic. Therefore, this is a more gentle introduction to K-means clustering. Here you will find out what K-Means Clustering, an algorithm, actually does. You will get only the basics, but in this particular topic, the extensions are not wildely different.

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Most popular machine learning R packages

The good thing about using open-source software is the community around it. There are very many R packages online, and recently CRAN package download logs were released. This means we can have a look at the number of downloads for each package, so to get a good feel for their relative popularity. I pulled the log files from the server and checked a few packages which are known to be related to machine learning. With this post you can see which are the community favorites, and get a feel for the R-software trend growth.

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