@phdthesis{VollmannPina2016,
  type      = {Master Thesis},
  author    = {Jos{\´e} Alberto Vollmann Pi{\~n}a},
  title     = {Clustering variants using an EM approach},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mit1-opus4-87378},
  year      = {2016},
  abstract  = {It is possible to obtain a common updating rule for k-means and Neural Gas algorithms by using a generalized Expectation Maximization method. This result is used to derive two variants of these methods. The use of a similarity measure, specifically the gaussian function, provides another clustering alternative to the before mentioned methods. The main benefit of using the gaussian function is that it inherently looks for a common cluster center for similar data points (depending on the value of the parameter s ). In different experiments we report similar behaviour of batch and proposed variants. Also we show some useful results for the “alternative” similarity method, specifically when there is no clue about the number of clusters in the data sets.},
  language  = {en}
}