Clustering with missing values

Simiński, Krzysztof

Krzysztof Simiński

Abstract:

The paper presents the clustering algorithm for data with missing values. In this approach both marginalisation and imputation are applied. The result of the clustering is the type-2 fuzzy set / rough fuzzy set. This approach enables the distinction between original and imputed data. The method can be applied to the data sets with all attributes lacking some values. The paper is accompanied by the numerical examples of clustering of synthetic and real-life data sets.

Reference:

Krzysztof Simiński, Clustering with missing values, [in] Fundamenta Informaticae, 2013, volume 123, number 3, pp. 331-350. ([20 pkt])

Bibtex Entry:

@ARTICLE{id:Siminski2013Clustering,
  author = {Krzysztof Simi\'{n}ski},
  title = {Clustering with missing values},
  journal = {Fundamenta Informaticae},
  year = {2013},
  volume = {123},
  pages = {331-350},
  number = {3},
  abstract = {The paper presents the clustering algorithm for data with missing
	values. In this approach both marginalisation and imputation are
	applied. The result of the clustering is the type-2 fuzzy set / rough
	fuzzy set. This approach enables the distinction between original
	and imputed data. The method can be applied to the data sets with
	all attributes lacking some values. The paper is accompanied by the
	numerical examples of clustering of synthetic and real-life data
	sets.},
   note = {[20 pkt]},
}