### Table of Contents

# Vector quantization / Clustering

##### References

## Sequential leader

For every new sample :

- if the distance between the sample and a cluster is smaller than a given threshold, then add the sample to the cluster, else create a new cluster with the sample

## Pairwise Clustering

Initially every sample is a cluster.

Repeat until the desired number of clusters is obtained :

- merge the two closest clusters

## k-means

*k-moyennes*

Randomly chose k clusters.

For every sample :

- decrease the distance between the closest cluster to the sample, and the sample (using a learning rate)
- decrease the learning rate in time

### k-means++

A variant that initializes centers so that there is a guarantee in accuracy, and a faster convergence :

- chose the first center randomly with uniform distribution among the samples
- chose the next centers randomly with probability proportional to the minimum distance of the sample to the already chosen centers.

##### References

### Elbow criterion

A way to chose the optimal number of clusters k.

Compute for different number of clusters the ratio of the intra-clusters variance to the total variance. The optimal number of clusters is when adding clusters do not bring significant decrease of the ratio.

## GNG (Growing Neural Gas)

## Kohonen auto-organizing maps

*Cartes auto-organisatrices de Kohonen*