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Discovering User Behavioral Rules Based on Multi-Dimensional Contexts

In: Context-Aware Machine Learning and Mobile Data Analytics

Author

Listed:
  • Iqbal H. Sarker

    (Swinburne University of Technology
    Chittagong University of Engineering & Technology)

  • Alan Colman

    (Swinburne University of Technology)

  • Jun Han

    (Swinburne University of Technology)

  • Paul Watters

    (Macquarie University
    Cyberstronomy Pty Ltd)

Abstract

In the previous chapter, we have presented an approach for discovering time-dependent rules of individual mobile phone users based on their unique behavioral patterns. In this chapter, we focus on discovering behavioral rules of individual mobile phone users by taking into account multi-dimensional contexts—for example temporal, spatial, or social context. Association rule mining is the most prominent rule-based machine learning method for generating rules for a particular constraint preference utilizing a given dataset. However, it generates various uninteresting contextual associations which lead to output vast number of redundant rules that become ineffective in making context-aware decisions. This redundant generation not only makes the rule set unnecessarily large in size, but also complicates the decision-making process in a context-aware system. In this chapter, we present an effective rule-based machine learning method that minimizes the issue and generates a set of non-redundant behavioral rules by taking into account the precedence of relevant contexts. Finally, the effectiveness of the technique presented in this chapter, has been provided through experimental results.

Suggested Citation

  • Iqbal H. Sarker & Alan Colman & Jun Han & Paul Watters, 2021. "Discovering User Behavioral Rules Based on Multi-Dimensional Contexts," Springer Books, in: Context-Aware Machine Learning and Mobile Data Analytics, chapter 0, pages 93-111, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-88530-4_6
    DOI: 10.1007/978-3-030-88530-4_6
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