# Articles by Stanislas Morbieu

### Animate intermediate results of your algorithm

February 19, 2019 |

The R package gganimate enables to animate plots. It is particularly interesting to visualize the intermediate results of an algorithm, to see how it converges towards the final results. The following illustrates this with K-means clustering. The outline of this post is as follows: We will first generate some artificial ... [Read more...]

### Animate intermediate results of your algorithm

February 19, 2019 |

The R package gganimate enables to animate plots. It is particularly interesting to visualize the intermediate results of an algorithm, to see how it converges towards the final results. The following illustrates this with K-means clustering. The outline of this post is as follows: We will first generate some artificial ... [Read more...]

### Chaining effect in clustering

January 21, 2019 |

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 ...

### How many red Christmas baubles on the tree?

January 5, 2019 |

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 ...

### Gaussian mixture models: k-means on steroids

December 22, 2018 |

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 not all about sunshines and rainbows

December 9, 2018 |

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 ...