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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2013: Intelligent Data Engineering and Automated Learning – IDEAL 2013 pp 142–152Cite as

Customer Unification in E-Commerce

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

Data mining applied to social media is gaining popularity. It is worth noticing that most e-commerce services also cause the formation of small communities not only services oriented toward socializing people. The results of their analysis are easier to implement. Besides, we can expect a better perception of the business by its own users, therefore the analysis of their behavior is justified. In the paper we introduce an algorithm which identifies particular customers among not logged or not registered users of a given e-commerce service. The identification of a customer is based on data that was given so as to accomplish selling procedure. Customers rarely use exactly the same identification data each time. In consequence, it is possible to check if customers create a group of unrelated individuals or if there are symptoms of social behavior.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Marcin Gorawski, Aleksander Chrószcz & Anna Gorawska

  2. Institute of Computer Science, Wroclaw University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Marcin Gorawski

Authors
  1. Marcin Gorawski
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  2. Aleksander Chrószcz
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  3. Anna Gorawska
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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Gorawski, M., Chrószcz, A., Gorawska, A. (2013). Customer Unification in E-Commerce. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_18

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_18

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  • Online ISBN: 978-3-642-41278-3

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