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A Two Tiered Finite Mixture Modelling Framework to Cluster Customers on EFTPOS Network

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AI 2015: Advances in Artificial Intelligence (AI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9457))

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Abstract

This paper proposes a framework to build a clustering model of customers of the retailers on the EFTPOS network of a major bank in Australia. The framework consists of two clustering tiers using Finite Mixture Modelling (FMM) that segments customers based on their probabilities of generating transactions of different categories. The first tier generates the transaction categories and the second tier segments the customers, each with a vector of the fractions of their transaction categories as parameters. For each tier, we determine the optimal number of clusters based on the Minimum Message Length (MML) criterion. With the premise that the most valuable customer segment is one that is most likely to generate the most valuable transaction category, we rank the customer segments based on their respective joint probabilities with the most valuable transaction category. By doing so, we are able to reveal the relative value of each customer segment.

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Authors and Affiliations

  1. Faculty of Information Technology Caulfield, Monash University, Melbourne, Australia

    Yuan Jin & Grace Rumantir

Authors
  1. Yuan Jin
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  2. Grace Rumantir
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Corresponding author

Correspondence to Grace Rumantir .

Editor information

Editors and Affiliations

  1. The University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

  2. The Australian National University, Canberra, Aust Capital Terr, Australia

    Jochen Renz

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Jin, Y., Rumantir, G. (2015). A Two Tiered Finite Mixture Modelling Framework to Cluster Customers on EFTPOS Network. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_24

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  • DOI: https://doi.org/10.1007/978-3-319-26350-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26349-6

  • Online ISBN: 978-3-319-26350-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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