Extracting additive and multiplicative coherent biclusters with swarm intelligence   [SI]

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Franca, F., D. and Zuben, F., J., V.

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Info: Proceedings of the 2011 IEEE Congress on Evolutionary Computation (Conference proceedings), 2011, p. 625-631
Keywords:Classification, clustering, data analysis, data mining, Data mining
Abstract:
Biclustering is usually referred to as the process of finding subsets of rows and columns from a given dataset expressing a relationship. Each subset is a bicluster and corresponds to a sub-matrix whose elements tend to present a high degree of coherence with each other, that may lead to novel discoveries regarding the objects in the dataset. This coherence leads to the possibility of obtaining representative values for rows (subset of objects) and columns (subset of attributes) of each bicluster. In the literature, it is usually studied the additive coherence among elements, i.e. each element is represented by the sum of its respective representative values. But in a given dataset, it is also possible to find multiplicative relations, i.e. each element being represented by the multiplication of its respective representative values, and that may reveal distinct knowledge contained in the objects of the dataset. So, in this paper, a swarm-based approach, named SwarmBcluster, is adapted to find both additive and multiplicative coherent biclusters from a dataset, in an attempt to enrich the amount of information provided by the biclusters. Experiments are performed considering two well-known datasets and it is found that the multiplicative coherence biclusters improve the quality of the data analysis [DA] and may contribute to reduce the influence of noise.
Notes:
CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
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BibTex:
@InProceedings{de-Franca:2011:Eaamcbwsi,
  title     = {Extracting additive and multiplicative coherent biclusters with swarm intelligence},
  author    = {Fabricio {de Franca} and Fernando J. {Von Zuben}},
  pages     = {625--631},
  booktitle = {Proceedings of the 2011 IEEE Congress on Evolutionary Computation},
  year      = {2011},
  editor = "Alice E. Smith",
  month     = {5-8 June},
  address   = {New Orleans, USA},
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  ISBN      = {0-7803-8515-2},
  keywords  = {Classification, clustering, data analysis and data mining, Data mining},
  abstract  = {
Biclustering is usually referred to as the process of finding subsets of rows
and columns from a given dataset expressing a relationship. Each subset is a
bicluster and corresponds to a sub-matrix whose elements tend to present a
high degree of coherence with each other, that may lead to novel discoveries
regarding the objects in the dataset. This coherence leads to the possibility
of obtaining representative values for rows (subset of objects) and columns
(subset of attributes) of each bicluster. In the literature, it is usually
studied the additive coherence among elements, i.e. each element is
represented by the sum of its respective representative values. But in a given
dataset, it is also possible to find multiplicative relations, i.e. each
element being represented by the multiplication of its respective
representative values, and that may reveal distinct knowledge contained in the
objects of the dataset. So, in this paper, a swarm-based approach, named
SwarmBcluster, is adapted to find both additive and multiplicative coherent
biclusters from a dataset, in an attempt to enrich the amount of information
provided by the biclusters. Experiments are performed considering two
well-known datasets and it is found that the multiplicative coherence
biclusters improve the quality of the data analysis and may contribute to
reduce the influence of noise.
},
  notes =	{CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET. },
}