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A Comparative Study in Data Mining: Clustering and Classification Capabilities

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Information Systems (EMCIS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 299))

Abstract

The ICT evolution has driven on the creation of a capable society, in providing new kinds and type of information. The gathered information is stored continuously, meaning that a great amount of databases has to be created. The problem that arises is whether there is a global manner of managing and gaining knowledge out of the rising variety and volumes of data. Many efforts have been developed for addressing the emerging challenges of data mining based on statistics and machine learning techniques that can significantly boost the ability to analyze data. In this paper, a detailed study on the data mining field takes place, followed by a comparative study between clustering and classification techniques, resulting that the integration of clustering and classification techniques can provide more accurate results than a simple classification technique that classifies datasets with priorly known attributes and classes.

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Acknowledgements

The CrowdHEALTH project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727560. Athanasios Kiourtis would also like to acknowledge the financial support from the “Foundation for Education and European Culture (IPEP)”.

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Authors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Argyro Mavrogiorgou, Athanasios Kiourtis, Dimosthenis Kyriazis & Marinos Themistocleous

Authors
  1. Argyro Mavrogiorgou
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  2. Athanasios Kiourtis
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  3. Dimosthenis Kyriazis
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  4. Marinos Themistocleous
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Corresponding author

Correspondence to Argyro Mavrogiorgou .

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Editors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Marinos Themistocleous

  2. Department of Management and Technology, Bocconi University, Milan, Italy

    Vincenzo Morabito

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Mavrogiorgou, A., Kiourtis, A., Kyriazis, D., Themistocleous, M. (2017). A Comparative Study in Data Mining: Clustering and Classification Capabilities. In: Themistocleous, M., Morabito, V. (eds) Information Systems. EMCIS 2017. Lecture Notes in Business Information Processing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-65930-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-65930-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65929-9

  • Online ISBN: 978-3-319-65930-5

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