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AIM in Unsupervised Data Mining

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Artificial Intelligence in Medicine

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Abstract

This chapter explores the differences between association rules extracted using the likelihood mining criterion (LMC) and rules extracted by using frequent item-set rule mining (FRM). LMC provides a change in perspective for rule selection, from a measure of frequency in the dataset to a measure of relationship between the rule items. For illustration, this chapter presents the evaluation of qualitative differences between LMC and FRM rules with three examples: (1) a basic rule mining scenario to illustrate LMC properties, (2) an analysis relating socioeconomic information and chemical exposure data, and (3) mining behavior routines in patients undergoing neurological rehabilitation. Results show that LMC is capable of extracting rare rules and does not suffer from support dilution. Furthermore, LMC focuses on the individual event generating processes, while FRM focuses on their commonalities.

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Acknowledgments

The authors are thankful for the permission to utilize the datasets used for illustration in this chapter.

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Authors and Affiliations

  1. Friedrich Alexander University Erlangen-Nuremberg, Erlangen, Germany

    Luis I. Lopera González, Adrian Derungs & Oliver Amft

Authors
  1. Luis I. Lopera González
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  2. Adrian Derungs
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  3. Oliver Amft
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Corresponding author

Correspondence to Luis I. Lopera González .

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Editors and Affiliations

  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

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Lopera González, L.I., Derungs, A., Amft, O. (2021). AIM in Unsupervised Data Mining. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_300-1

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  • DOI: https://doi.org/10.1007/978-3-030-58080-3_300-1

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  • Print ISBN: 978-3-030-58080-3

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