Biclustering in data mining using a memetic multi-objective evolutionary algorithm

In this paper, a new memetic strategy that integrates a multi-objective evolutionary algorithm (the SPEA2) with a local search technique for data mining is presented. The algorithm explores a Term Frequency-Inverse Document Frequency (TF-IDF) data matrix in order to find biclusters that fulfill seve...

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Detalles Bibliográficos
Autores principales: Gallo, Cristian Andrés, Maguitman, Ana Gabriela, Carballido, Jessica Andrea, Ponzoni, Ignacio
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2008
Materias:
Ciencias Informáticas
biclustering
evolutionary algorithms
Data mining
Algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/21682
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-21682
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
biclustering
evolutionary algorithms
Data mining
Algorithms
spellingShingle Ciencias Informáticas
biclustering
evolutionary algorithms
Data mining
Algorithms
Gallo, Cristian Andrés
Maguitman, Ana Gabriela
Carballido, Jessica Andrea
Ponzoni, Ignacio
Biclustering in data mining using a memetic multi-objective evolutionary algorithm
topic_facet Ciencias Informáticas
biclustering
evolutionary algorithms
Data mining
Algorithms
description In this paper, a new memetic strategy that integrates a multi-objective evolutionary algorithm (the SPEA2) with a local search technique for data mining is presented. The algorithm explores a Term Frequency-Inverse Document Frequency (TF-IDF) data matrix in order to find biclusters that fulfill several objectives. The case of study was a dataset corresponding to the Reuters-21578 corpus. Our algorithm performed satisfactorily, finding biclusters that have large size and coherent values, yielding to undeniably promising outcomes. Nonetheless, more experiments with data from other corpus are necessary, thus leading to more concluding results
format Objeto de conferencia
Objeto de conferencia
author Gallo, Cristian Andrés
Maguitman, Ana Gabriela
Carballido, Jessica Andrea
Ponzoni, Ignacio
author_facet Gallo, Cristian Andrés
Maguitman, Ana Gabriela
Carballido, Jessica Andrea
Ponzoni, Ignacio
author_sort Gallo, Cristian Andrés
title Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_short Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_full Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_fullStr Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_full_unstemmed Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_sort biclustering in data mining using a memetic multi-objective evolutionary algorithm
publishDate 2008
url http://sedici.unlp.edu.ar/handle/10915/21682
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AT maguitmananagabriela biclusteringindataminingusingamemeticmultiobjectiveevolutionaryalgorithm
AT carballidojessicaandrea biclusteringindataminingusingamemeticmultiobjectiveevolutionaryalgorithm
AT ponzoniignacio biclusteringindataminingusingamemeticmultiobjectiveevolutionaryalgorithm
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