In a previous blog post, I explained how we can leverage the k-means clustering algorithm to count the number of red baubles on a Christmas tree. This method fails however if we put Christmas tinsels on it. Let’s find a solution for this more difficult case. Filter red points ...

Christmas time is over. It is time to remove the Cristmas tree. But just before removing it, one can ask: How many red Christmas baubles are on the tree? In order to answer this question, we will proceed with the following steps: Transform the picture into a dataframe, which is ...

The k-means algorithm assumes the data is generated by a mixture of Gaussians, each having the same proportion and variance, and no covariance. These assumptions can be alleviated with a more generic algorithm: the CEM algorithm applied on a mixture of Gaussians. To illustrate this, we will first apply a ...

K-means is the most known and used clustering algorithm. It has however several drawbacks and does not behave nicely on some datasets. In fact, every clustering algorithm has its own strenghts and drawbacks. Each relies on some assumptions on the dataset and leverages these properties to cluster the data into ...

In order to understand how a clustering algorithm works, good sample datasets are useful to highlight its behavior under certain circumstances. This post shows how to generate 9 datasets: a mixture of two Gaussians with same size, variance and no covariance, Gaussians which differ only from their means and sizes, Gaussians ...