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Automatic labeling of social network users Scientia.Net through the machine learning supervised application

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Abstract

This work evaluates clustering techniques and pattern classification and proposes a model that uses a combination of supervised learning algorithms and unsupervised learning ones with the goal of creating groups and identifying which attributes can define them (labeling) applied to social network called Scientia.Net. The tests were done using a database with around 2000 users. The proposed model shows the results applied to the database Scientia.Net.

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Notes

  1. A knowledge extraction (KDD) tool that includes a series of algorithms for data preparation, machine learning (mining) and results validation.

  2. Coordination of improvement of higher education personnel—http://www.capes.gov.br.

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Authors and Affiliations

  1. Departamento de Computação, Universidade Federal do Piauí, Teresina, PI, Brasil

    Bruno Vicente Alves de Lima, Vinicius Ponte Machado & Lucas Araújo Lopes

Authors
  1. Bruno Vicente Alves de Lima
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  2. Vinicius Ponte Machado
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  3. Lucas Araújo Lopes
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Corresponding author

Correspondence to Bruno Vicente Alves de Lima.

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de Lima, B.V.A., Machado, V.P. & Lopes, L.A. Automatic labeling of social network users Scientia.Net through the machine learning supervised application. Soc. Netw. Anal. Min. 5, 44 (2015). https://doi.org/10.1007/s13278-015-0285-x

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  • DOI: https://doi.org/10.1007/s13278-015-0285-x

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