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Association Rule Mining in Fuzzy Political Donor Communities

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10935))

Abstract

Social networks can be found in many domains. While community analysis can help users understand relationships within the network, it can be difficult to analyze the results without considerable effort. However, using the communities to partition underlying data, it is possible to use association rule mining to more easily facilitate meaningful analysis. In this paper we use a real-world dataset drawn from political campaign contributions. The network of donations is treated as a social network and fuzzy hierarchical community detection is applied to the data. The resulting communities are then analyzed with association rule mining to find distinguishing features within the resulting communities. The results show the mined rules help identify notable features for the communities and aid in understanding both shared and differing community characteristics.

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Notes

  1. 1.

    Based on 2016 data from https://www.followthemoney.org.

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Authors and Affiliations

  1. Gianforte School of Computing, Montana State University, Bozeman, MT, 59717, USA

    Scott Wahl & John Sheppard

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  1. Scott Wahl
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  2. John Sheppard
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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Wahl, S., Sheppard, J. (2018). Association Rule Mining in Fuzzy Political Donor Communities. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_18

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  • DOI: https://doi.org/10.1007/978-3-319-96133-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96132-3

  • Online ISBN: 978-3-319-96133-0

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