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The Cassiopeia Model: A study with other algorithms for attribute selection in text clusterization

This work proposes a study of algorithms used for attribute selection in text clusterization in the scientific literature and selection of attribute in the Cassiopeia model The most relevant contributions include the use of summarized texts as an entrance in pre-processing stage of clusterization, language independence with the use of stop words and the treatment of high dimensionality, a problem that is inherent to Text Mining. Hence, our intention is to achieve an improvement in the measurement of clusters as well as to solve the problem of high dimensionality.