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A Survey on Open Source Data Mining Tools for SMEs

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New Advances in Information Systems and Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 444))

Abstract

Data Mining is a software tool dedicated to scan data repositories, generate information, and discover knowledge. Currently, the traditional data processing tools and its applications are not capable of managing the massive amounts of data available inside SMEs environments. Therefore, it is critical to use effective and efficient Data Mining tools which represent a valuable support for SMEs decision-making. In this paper we describe and analyze seven popular open source data mining tools—KEEL, KNIME, Orange, RapidMiner, R Project, Tanagra and WEKA.

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Author information

Authors and Affiliations

  1. ISEC—Superior Institute of Engineering of Coimbra, Polytechnic of Coimbra, 3030-190, Coimbra, Portugal

    Pedro Almeida & Jorge Bernardino

  2. CISUC— Centre of Informatics and Systems of the University of Coimbra, 3030-290, Coimbra, Portugal

    Jorge Bernardino

Authors
  1. Pedro Almeida
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  2. Jorge Bernardino
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Corresponding author

Correspondence to Pedro Almeida .

Editor information

Editors and Affiliations

  1. DEI/FCT, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. ISEGI,, Universidade Nova de Lisboa, Lisboa, Portugal

    Ana Maria Correia

  3. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  4. DSI, Universidade do Minho, Guimarães, Portugal

    Luis Paulo Reis

  5. Rua Dom Manoel de Medeiros, s/n, DoisIrm, Universidade Federal Rural de Pernambuco, Recife, Brazil

    Marcelo Mendonça Teixeira

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© 2016 Springer International Publishing Switzerland

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Almeida, P., Bernardino, J. (2016). A Survey on Open Source Data Mining Tools for SMEs. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-31232-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-31232-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31231-6

  • Online ISBN: 978-3-319-31232-3

  • eBook Packages: EngineeringEngineering (R0)

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