@ARTICLE{id:Siminski2012Clustering,
  author = {Krzysztof Simi\'{n}ski},
  title = {Clustering in Fuzzy Subspaces},
  journal = {Theoretical and Applied Informatics},
  year = {2012},
  volume = {24},
  pages = {313--326},
  number = {4},
  abstract = {Some data sets contain data clusters not in all dimension, but in
	subspaces. Known algorithms select attributes and identify clusters
	in subspaces. The paper presents a novel algorithm for subspace fuzzy
	clustering. Each data example has fuzzy membership to the cluster.
	Each cluster is defined in a certain subspace, but the the membership
	of the descriptors of the cluster to the subspace (called descriptor
	weight) is fuzzy (from interval [0, 1]) – the descriptors of the
	cluster can have partial membership to a subspace the cluster is
	defined in. Thus the clusters are fuzzy defined in their subspaces.
	The clusters are defined by their centre, fuzziness and weights of
	descriptors. The clustering algorithm is based on minimizing of criterion
	function. The paper is accompanied by the experimental results of
	clustering. This approach can be used for partition of input domain
	in extraction rule base for neuro-fuzzy systems.},
  doi = {10.2478/v10179-012-0019-y},
   note = {[4 pkt]},
}