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

ai/methods/clustering.txt · Last modified: 2013/09/19 16:40 (external edit)
 
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