====== Vector quantization / Clustering ======
==References==
  - [[http://www.comp.lancs.ac.uk/~kristof/research/notes/clustr/index.html|Methods Overview [EN] ]]

===== 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 ==
  * [[http://www.stanford.edu/~sergeiv/papers/kMeansPP-soda.pdf|2006, Arthur-Vassilvitskii, k-means++: The Advantages of Careful Seeding]]

==== 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//